Restructure: Move grid_* + vocab_* into packages (klausur-service)

grid/ package (16 files):
  grid/build/   — core, zones, cleanup, text_ops, cell_ops, finalize
  grid/editor/  — api, helpers, columns, filters, headers, zones

vocab/ package (10 files):
  vocab/worksheet/ — api, models, extraction, generation, ocr, upload, analysis, compare
  vocab/           — session_store, learn_bridge

26 backward-compat shims. Internal imports relative. RAG untouched.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

.claude/rules/loc-exceptions.txt

@@ -31,6 +31,7 @@
 
 # Two indivisible route handlers (~230 LOC each) that cannot be split further
 **/vocab_worksheet_compare_api.py | owner=klausur | reason=compare_ocr_methods (234 LOC) + analyze_grid (255 LOC), each a single cohesive handler | review=2026-10-01
+**/vocab/worksheet/compare_api.py | owner=klausur | reason=Same file moved to vocab/ package | review=2026-10-01
 
 # TypeScript Data Catalogs (admin-lehrer/lib/sdk/)
 # Pure exported const arrays/objects with type definitions, no business logic.

klausur-service/backend/grid/__init__.py

@@ -0,0 +1,10 @@
+"""
+Grid package — restructured from grid_* flat modules.
+
+Backward-compatible re-exports: consumers can still use
+``from grid_build_core import ...`` etc. via the shim files in backend/.
+
+Sub-packages:
+  - grid.build   — grid construction pipeline (_build_grid_core and phases)
+  - grid.editor  — FastAPI endpoints, helper functions, column/zone logic
+"""

klausur-service/backend/grid/build/__init__.py

@@ -0,0 +1,11 @@
+"""
+Grid Build sub-package — grid construction pipeline.
+
+Modules:
+  - core      — _build_grid_core() main entry point
+  - zones     — image loading, graphic/box detection, zone-aware grid building
+  - cleanup   — junk row removal, artifact cleanup, pipe dividers
+  - text_ops  — color annotation, heading detection, IPA correction
+  - cell_ops  — bullet removal, garbled cells, word-box reordering
+  - finalize  — dictionary detection, spell checking, result assembly
+"""

klausur-service/backend/grid/build/cell_ops.py

@@ -0,0 +1,305 @@
+"""
+Grid Build Cell Ops — Cell-level operations: bullet/artifact removal,
+garbled cell cleanup, word-box reordering, and max_columns enforcement.
+
+Extracted from grid_build_core.py for maintainability.
+"""
+
+import logging
+import re
+from typing import Any, Dict, List, Tuple
+
+from cv_ocr_engines import (
+    _words_to_reading_order_text, _group_words_into_lines, _lookup_ipa,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _remove_bullets_and_artifacts(zones_data: List[Dict[str, Any]]) -> None:
+    """Remove blue bullet/artifact word_boxes (Step 5i).
+
+    Handles tiny coloured symbols, overlapping word_boxes, duplicate text,
+    and syllable-split word merging.
+    """
+    _ALPHA_WORD_RE = re.compile(r'^[A-Za-z\u00c0-\u024f\-]+[.,;:!?]*$')
+    _REMOVE_SYMBOLS = {'>', '<', '~', '\\', '^', '`', '#', '|', '¬', '¦'}
+
+    bullet_removed = 0
+    for z in zones_data:
+        for cell in z.get("cells", []):
+            wbs = cell.get("word_boxes") or []
+            if len(wbs) < 2:
+                continue
+            to_remove: set = set()
+
+            # Rule (a): tiny coloured symbols
+            for i, wb in enumerate(wbs):
+                cn = wb.get("color_name", "black")
+                if (cn != "black"
+                        and wb.get("width", 0) * wb.get("height", 0) < 200
+                        and wb.get("conf", 100) < 85):
+                    to_remove.add(i)
+
+            # Rule (a2): isolated non-alphanumeric symbols
+            for i, wb in enumerate(wbs):
+                t = (wb.get("text") or "").strip()
+                if t and not re.search(r'[a-zA-Z0-9äöüÄÖÜß]', t) and len(t) <= 2:
+                    if t in _REMOVE_SYMBOLS:
+                        to_remove.add(i)
+
+            # Rule (b) + (c): overlap and duplicate detection
+            to_merge: List[Tuple[int, int]] = []
+            indexed = sorted(enumerate(wbs), key=lambda iw: iw[1].get("left", 0))
+            for p in range(len(indexed) - 1):
+                i1, w1 = indexed[p]
+                i2, w2 = indexed[p + 1]
+                x1s, x1e = w1.get("left", 0), w1.get("left", 0) + w1.get("width", 0)
+                x2s, x2e = w2.get("left", 0), w2.get("left", 0) + w2.get("width", 0)
+                overlap = max(0, min(x1e, x2e) - max(x1s, x2s))
+                min_w = min(w1.get("width", 1), w2.get("width", 1))
+                gap = x2s - x1e
+                overlap_pct = overlap / min_w if min_w > 0 else 0
+
+                if overlap_pct > 0.20:
+                    t1 = (w1.get("text") or "").strip()
+                    t2 = (w2.get("text") or "").strip()
+
+                    # Syllable-split words
+                    if (overlap_pct <= 0.75
+                            and _ALPHA_WORD_RE.match(t1)
+                            and _ALPHA_WORD_RE.match(t2)):
+                        to_merge.append((i1, i2))
+                        continue
+
+                    # High overlap with short prefix
+                    if (overlap_pct > 0.75
+                            and _ALPHA_WORD_RE.match(t1)
+                            and _ALPHA_WORD_RE.match(t2)
+                            and t1.rstrip(".,;:!?").lower() != t2.rstrip(".,;:!?").lower()
+                            and min(len(t1.rstrip(".,;:!?")), len(t2.rstrip(".,;:!?"))) <= 4):
+                        to_merge.append((i1, i2))
+                        continue
+
+                    if overlap_pct <= 0.40:
+                        continue
+
+                    c1 = w1.get("conf", 50)
+                    c2 = w2.get("conf", 50)
+
+                    # Very high overlap: prefer IPA-dictionary word
+                    if overlap_pct > 0.90 and t1.lower() != t2.lower():
+                        in_dict_1 = bool(_lookup_ipa(re.sub(r'[²³¹\d/]', '', t1.lower()), "british")) if t1.isalpha() else False
+                        in_dict_2 = bool(_lookup_ipa(re.sub(r'[²³¹\d/]', '', t2.lower()), "british")) if t2.isalpha() else False
+                        if in_dict_1 and not in_dict_2:
+                            to_remove.add(i2)
+                            continue
+                        elif in_dict_2 and not in_dict_1:
+                            to_remove.add(i1)
+                            continue
+
+                    if c1 < c2:
+                        to_remove.add(i1)
+                    elif c2 < c1:
+                        to_remove.add(i2)
+                    else:
+                        if w1.get("height", 0) > w2.get("height", 0):
+                            to_remove.add(i1)
+                        else:
+                            to_remove.add(i2)
+
+                elif (gap < 6
+                      and w1.get("color_name") == "blue"
+                      and w2.get("color_name") == "blue"
+                      and (w1.get("text") or "").strip() == (w2.get("text") or "").strip()):
+                    c1 = w1.get("conf", 50)
+                    c2 = w2.get("conf", 50)
+                    to_remove.add(i1 if c1 <= c2 else i2)
+
+            # Execute merges first (syllable-split words)
+            if to_merge:
+                merge_parent: Dict[int, int] = {}
+                for mi1, mi2 in to_merge:
+                    actual_mi1 = mi1
+                    while actual_mi1 in merge_parent:
+                        actual_mi1 = merge_parent[actual_mi1]
+                    if actual_mi1 in to_remove or mi2 in to_remove:
+                        continue
+                    if mi2 in merge_parent:
+                        continue
+                    mw1, mw2 = wbs[actual_mi1], wbs[mi2]
+                    mt1 = (mw1.get("text") or "").rstrip(".,;:!?")
+                    mt2 = (mw2.get("text") or "").strip()
+                    merged_text = mt1 + mt2
+                    mx = min(mw1["left"], mw2["left"])
+                    my = min(mw1["top"], mw2["top"])
+                    mr = max(mw1["left"] + mw1["width"],
+                             mw2["left"] + mw2["width"])
+                    mb = max(mw1["top"] + mw1["height"],
+                             mw2["top"] + mw2["height"])
+                    mw1["text"] = merged_text
+                    mw1["left"] = mx
+                    mw1["top"] = my
+                    mw1["width"] = mr - mx
+                    mw1["height"] = mb - my
+                    mw1["conf"] = (mw1.get("conf", 50) + mw2.get("conf", 50)) / 2
+                    to_remove.add(mi2)
+                    merge_parent[mi2] = actual_mi1
+                    bullet_removed -= 1
+
+            if to_remove:
+                bullet_removed += len(to_remove)
+                filtered = [wb for i, wb in enumerate(wbs) if i not in to_remove]
+                cell["word_boxes"] = filtered
+                if not cell.get("_ipa_corrected"):
+                    cell["text"] = _words_to_reading_order_text(filtered)
+
+    if bullet_removed:
+        for z in zones_data:
+            z["cells"] = [c for c in z.get("cells", [])
+                          if (c.get("word_boxes") or c.get("text", "").strip())]
+        logger.info("Step 5i: removed %d bullet/artifact word_boxes", bullet_removed)
+
+
+def _remove_garbled_cells(zones_data: List[Dict[str, Any]]) -> None:
+    """Remove cells whose text is entirely garbled / artifact noise (Step 5j-pre)."""
+    _COMMON_SHORT_WORDS = {
+        "ab", "am", "an", "da", "du", "er", "es", "im", "in", "ja",
+        "ob", "so", "um", "zu", "wo", "je", "oh", "or",
+        "die", "der", "das", "dem", "den", "des", "ein", "und",
+        "auf", "aus", "bei", "bis", "für", "mit", "nur", "von",
+        "a", "i", "an", "as", "at", "be", "by", "do", "go", "he",
+        "if", "in", "is", "it", "me", "my", "no", "of", "oh", "ok",
+        "on", "or", "so", "to", "up", "us", "we",
+        "the", "and", "but", "for", "not",
+    }
+    _PURE_JUNK_RE = re.compile(r'^[\W\d\s]+$')
+    artifact_cells_removed = 0
+
+    for z in zones_data:
+        before = len(z.get("cells", []))
+        kept = []
+        for cell in z.get("cells", []):
+            text = (cell.get("text") or "").strip()
+            core = text.rstrip(".,;:!?'\"")
+            is_artifact = False
+            if not core:
+                is_artifact = True
+            elif _PURE_JUNK_RE.match(core):
+                if core.strip() not in ('=', '(=', '=)', ';', ':', '-', '–', '—'):
+                    is_artifact = True
+            elif len(core) <= 2 and core.lower() not in _COMMON_SHORT_WORDS and not core.isalpha():
+                is_artifact = True
+            elif len(core) <= 3 and core.isupper() and core.lower() not in _COMMON_SHORT_WORDS:
+                is_artifact = True
+            elif (len(core) <= 5 and re.search(r'\d', core) and re.search(r'[A-Za-z]', core)
+                  and not re.match(r'^[pPsS]\.?\d+$', core)):
+                is_artifact = True
+            if is_artifact:
+                kept.append(None)
+            else:
+                kept.append(cell)
+        z["cells"] = [c for c in kept if c is not None]
+        artifact_cells_removed += before - len(z["cells"])
+
+    if artifact_cells_removed:
+        for z in zones_data:
+            cell_ris = {c.get("row_index") for c in z.get("cells", [])}
+            z["rows"] = [r for r in z.get("rows", []) if r["index"] in cell_ris]
+        logger.info("Step 5j-pre: removed %d artifact cells", artifact_cells_removed)
+
+
+def _normalize_word_order(zones_data: List[Dict[str, Any]]) -> None:
+    """Normalise word_box order to reading order (Step 5j)."""
+    wb_reordered = 0
+    for z in zones_data:
+        for cell in z.get("cells", []):
+            wbs = cell.get("word_boxes") or []
+            if len(wbs) < 2:
+                continue
+            lines = _group_words_into_lines(wbs, y_tolerance_px=15)
+            sorted_wbs = [w for line in lines for w in line]
+            if [id(w) for w in sorted_wbs] != [id(w) for w in wbs]:
+                cell["word_boxes"] = sorted_wbs
+                wb_reordered += 1
+    if wb_reordered:
+        logger.info("Step 5j: re-ordered word_boxes in %d cells to reading order", wb_reordered)
+
+
+def _enforce_max_columns(
+    zones_data: List[Dict[str, Any]],
+    max_columns: int,
+) -> None:
+    """Enforce max_columns by merging narrowest columns (Step 5k)."""
+    for z in zones_data:
+        if z.get("zone_type") != "content":
+            continue
+        cols = z.get("columns", [])
+        cells = z.get("cells", [])
+        if len(cols) <= max_columns:
+            continue
+
+        logger.info(
+            "max_columns=%d: zone %s has %d columns -> merging",
+            max_columns, z.get("zone_index"), len(cols),
+        )
+
+        cols_by_width = sorted(cols, key=lambda c: (c.get("x_max_px", 0) - c.get("x_min_px", 0)))
+
+        while len(cols) > max_columns:
+            narrowest = cols_by_width.pop(0)
+            ni = narrowest["index"]
+
+            sorted_by_x = sorted(cols, key=lambda c: c.get("x_min_px", c.get("x_min_pct", 0)))
+            pos = next(i for i, c in enumerate(sorted_by_x) if c["index"] == ni)
+            if pos + 1 < len(sorted_by_x):
+                merge_target = sorted_by_x[pos + 1]
+            elif pos > 0:
+                merge_target = sorted_by_x[pos - 1]
+            else:
+                break
+
+            ti = merge_target["index"]
+
+            merge_target["x_min_px"] = min(
+                merge_target.get("x_min_px", merge_target.get("x_min_pct", 0)),
+                narrowest.get("x_min_px", narrowest.get("x_min_pct", 0)),
+            )
+            merge_target["x_max_px"] = max(
+                merge_target.get("x_max_px", merge_target.get("x_max_pct", 100)),
+                narrowest.get("x_max_px", narrowest.get("x_max_pct", 100)),
+            )
+            if "x_min_pct" in merge_target and "x_min_pct" in narrowest:
+                merge_target["x_min_pct"] = min(merge_target["x_min_pct"], narrowest["x_min_pct"])
+                merge_target["x_max_pct"] = max(merge_target["x_max_pct"], narrowest["x_max_pct"])
+
+            for cell in cells:
+                if cell.get("col_index") == ni:
+                    cell["col_index"] = ti
+                    existing = next(
+                        (c for c in cells if c["col_index"] == ti
+                         and c["row_index"] == cell["row_index"]
+                         and c is not cell),
+                        None,
+                    )
+                    if existing:
+                        existing["text"] = (
+                            (existing.get("text", "") + " " + cell.get("text", "")).strip()
+                        )
+                        existing["word_boxes"] = existing.get("word_boxes", []) + cell.get("word_boxes", [])
+                        cell["_merged"] = True
+
+            z["cells"] = [c for c in cells if not c.get("_merged")]
+            cells = z["cells"]
+            cols.remove(narrowest)
+            cols_by_width = [c for c in cols_by_width if c["index"] != ni]
+
+        # Re-index columns 0..N-1
+        for new_idx, col in enumerate(sorted(cols, key=lambda c: c.get("x_min_px", c.get("x_min_pct", 0)))):
+            old_idx = col["index"]
+            col["index"] = new_idx
+            for cell in cells:
+                if cell.get("col_index") == old_idx:
+                    cell["col_index"] = new_idx
+
+        logger.info("max_columns: zone %s now has %d columns", z.get("zone_index"), len(cols))

klausur-service/backend/grid/build/cleanup.py

@@ -0,0 +1,390 @@
+"""
+Grid Build Cleanup — Phase 3: Junk row removal, artifact cleanup, pipe
+divider removal, connector normalization, border strip detection, and
+alphabet sidebar removal.
+
+Extracted from grid_build_core.py for maintainability.
+"""
+
+import logging
+import re
+from typing import Any, Dict, List
+
+from cv_ocr_engines import _words_to_reading_order_text
+
+logger = logging.getLogger(__name__)
+
+_PIPE_RE = re.compile(r"^\|+$")
+
+
+def _cleanup_zones(
+    zones_data: List[Dict[str, Any]],
+    border_prefiltered: bool,
+    session_id: str,
+) -> bool:
+    """Clean up zone data: remove junk rows, artifacts, pipes, border strips.
+
+    Args:
+        zones_data: List of zone dicts (modified in place).
+        border_prefiltered: Whether border words were already pre-filtered.
+        session_id: For logging.
+
+    Returns:
+        Updated border_prefiltered flag.
+    """
+    _remove_junk_rows(zones_data)
+    _remove_artifact_cells(zones_data)
+    _remove_oversized_word_boxes(zones_data)
+    _remove_pipe_dividers(zones_data)
+    _normalize_connector_columns(zones_data)
+    border_prefiltered = _remove_border_strips(zones_data, border_prefiltered)
+    _remove_alphabet_sidebars(zones_data)
+    return border_prefiltered
+
+
+def _remove_junk_rows(zones_data: List[Dict[str, Any]]) -> None:
+    """Remove rows where ALL cells contain only short, low-confidence text.
+
+    Also removes 'oversized stub' rows and 'scattered debris' rows.
+    """
+    _JUNK_CONF_THRESHOLD = 50
+    _JUNK_MAX_TEXT_LEN = 3
+
+    for z in zones_data:
+        cells = z.get("cells", [])
+        rows = z.get("rows", [])
+        if not cells or not rows:
+            continue
+
+        # Compute median word height across the zone for oversized detection
+        all_wb_heights = [
+            wb["height"]
+            for cell in cells
+            for wb in cell.get("word_boxes") or []
+            if wb.get("height", 0) > 0
+        ]
+        median_wb_h = sorted(all_wb_heights)[len(all_wb_heights) // 2] if all_wb_heights else 28
+
+        junk_row_indices = set()
+        for row in rows:
+            ri = row["index"]
+            row_cells = [c for c in cells if c.get("row_index") == ri]
+            if not row_cells:
+                continue
+
+            row_wbs = [
+                wb for cell in row_cells
+                for wb in cell.get("word_boxes") or []
+            ]
+
+            # Rule 1: ALL word_boxes are low-conf AND short text
+            all_junk = True
+            for wb in row_wbs:
+                text = (wb.get("text") or "").strip()
+                conf = wb.get("conf", 0)
+                if conf >= _JUNK_CONF_THRESHOLD or len(text) > _JUNK_MAX_TEXT_LEN:
+                    all_junk = False
+                    break
+            if all_junk and row_wbs:
+                junk_row_indices.add(ri)
+                continue
+
+            # Rule 2: oversized stub -- <=3 words, short total text,
+            # and word height > 1.8x median
+            if len(row_wbs) <= 3:
+                total_text = "".join((wb.get("text") or "").strip() for wb in row_wbs)
+                max_h = max((wb.get("height", 0) for wb in row_wbs), default=0)
+                has_page_ref = any(
+                    re.match(r'^[pPsS]\.?\s*\d+$', (wb.get("text") or "").strip())
+                    for wb in row_wbs
+                )
+                if len(total_text) <= 5 and max_h > median_wb_h * 1.8 and not has_page_ref:
+                    junk_row_indices.add(ri)
+                    continue
+
+            # Rule 3: scattered debris -- rows with only tiny fragments
+            longest = max(len((wb.get("text") or "").strip()) for wb in row_wbs)
+            if longest <= 2:
+                junk_row_indices.add(ri)
+                continue
+
+        if junk_row_indices:
+            z["cells"] = [c for c in cells if c.get("row_index") not in junk_row_indices]
+            z["rows"] = [r for r in rows if r["index"] not in junk_row_indices]
+            logger.info(
+                "build-grid: removed %d junk rows from zone %d: %s",
+                len(junk_row_indices), z["zone_index"],
+                sorted(junk_row_indices),
+            )
+
+
+def _remove_artifact_cells(zones_data: List[Dict[str, Any]]) -> None:
+    """Remove individual cells with a single very-short, low-conf word."""
+    _ARTIFACT_MAX_LEN = 2
+    _ARTIFACT_CONF_THRESHOLD = 65
+
+    for z in zones_data:
+        cells = z.get("cells", [])
+        if not cells:
+            continue
+        artifact_ids = set()
+        for cell in cells:
+            wbs = cell.get("word_boxes") or []
+            if len(wbs) != 1:
+                continue
+            wb = wbs[0]
+            text = (wb.get("text") or "").strip()
+            conf = wb.get("conf", 100)
+            if len(text) <= _ARTIFACT_MAX_LEN and conf < _ARTIFACT_CONF_THRESHOLD:
+                artifact_ids.add(cell.get("cell_id"))
+        if artifact_ids:
+            z["cells"] = [c for c in cells if c.get("cell_id") not in artifact_ids]
+            logger.info(
+                "build-grid: removed %d artifact cells from zone %d: %s",
+                len(artifact_ids), z.get("zone_index", 0),
+                [c.get("text") for c in cells if c.get("cell_id") in artifact_ids],
+            )
+
+
+def _remove_oversized_word_boxes(zones_data: List[Dict[str, Any]]) -> None:
+    """Remove word_boxes whose height is 3x+ the median (graphic artifacts)."""
+    for z in zones_data:
+        cells = z.get("cells", [])
+        if not cells:
+            continue
+        all_wh = [
+            wb["height"]
+            for cell in cells
+            for wb in cell.get("word_boxes") or []
+            if wb.get("height", 0) > 0
+        ]
+        if not all_wh:
+            continue
+        med_h = sorted(all_wh)[len(all_wh) // 2]
+        oversized_threshold = med_h * 3
+        removed_oversized = 0
+        for cell in cells:
+            wbs = cell.get("word_boxes") or []
+            filtered = [wb for wb in wbs if wb.get("height", 0) <= oversized_threshold]
+            if len(filtered) < len(wbs):
+                removed_oversized += len(wbs) - len(filtered)
+                cell["word_boxes"] = filtered
+                cell["text"] = _words_to_reading_order_text(filtered)
+        if removed_oversized:
+            z["cells"] = [c for c in cells if c.get("word_boxes")]
+            logger.info(
+                "build-grid: removed %d oversized word_boxes (>%dpx) from zone %d",
+                removed_oversized, oversized_threshold, z.get("zone_index", 0),
+            )
+
+
+def _remove_pipe_dividers(zones_data: List[Dict[str, Any]]) -> None:
+    """Remove pipe-character word_boxes (column divider artifacts)."""
+    for z in zones_data:
+        if z.get("vsplit_group") is not None:
+            continue  # pipes already removed before split
+        removed_pipes = 0
+        for cell in z.get("cells", []):
+            wbs = cell.get("word_boxes") or []
+            filtered = [wb for wb in wbs if not _PIPE_RE.match((wb.get("text") or "").strip())]
+            if len(filtered) < len(wbs):
+                removed_pipes += len(wbs) - len(filtered)
+                cell["word_boxes"] = filtered
+                cell["text"] = _words_to_reading_order_text(filtered)
+        if removed_pipes:
+            z["cells"] = [c for c in z.get("cells", []) if (c.get("word_boxes") or c.get("text", "").strip())]
+            logger.info(
+                "build-grid: removed %d pipe-divider word_boxes from zone %d",
+                removed_pipes, z.get("zone_index", 0),
+            )
+
+    # Strip pipe chars ONLY from cell edges (OCR artifacts).
+    # Preserve pipes embedded in words as syllable separators.
+    for z in zones_data:
+        for cell in z.get("cells", []):
+            text = cell.get("text", "")
+            if "|" in text:
+                cleaned = text.strip("|").strip()
+                if cleaned != text.strip():
+                    cell["text"] = cleaned
+
+
+def _normalize_connector_columns(zones_data: List[Dict[str, Any]]) -> None:
+    """Normalize narrow connector columns where OCR appends noise chars.
+
+    In synonym dictionaries a narrow column repeats the same word
+    (e.g. "oder") in every row. OCR sometimes appends noise chars.
+    """
+    for z in zones_data:
+        cols = z.get("columns", [])
+        cells = z.get("cells", [])
+        if not cols or not cells:
+            continue
+        for col in cols:
+            ci = col.get("index")
+            col_cells = [c for c in cells if c.get("col_index") == ci]
+            if len(col_cells) < 3:
+                continue
+            text_counts: Dict[str, int] = {}
+            for c in col_cells:
+                t = (c.get("text") or "").strip()
+                if t:
+                    text_counts[t] = text_counts.get(t, 0) + 1
+            if not text_counts:
+                continue
+            dominant_text = max(text_counts, key=text_counts.get)  # type: ignore[arg-type]
+            dominant_count = text_counts[dominant_text]
+            if len(dominant_text) > 10 or dominant_count < len(col_cells) * 0.6:
+                continue
+            fixed = 0
+            for c in col_cells:
+                t = (c.get("text") or "").strip()
+                if t != dominant_text and t.startswith(dominant_text) and len(t) <= len(dominant_text) + 2:
+                    c["text"] = dominant_text
+                    wbs = c.get("word_boxes") or []
+                    if len(wbs) == 1:
+                        wbs[0]["text"] = dominant_text
+                    fixed += 1
+            if fixed:
+                logger.info(
+                    "build-grid: normalized %d outlier cells in connector column %d "
+                    "(dominant='%s') zone %d",
+                    fixed, ci, dominant_text, z.get("zone_index", 0),
+                )
+
+
+def _remove_border_strips(
+    zones_data: List[Dict[str, Any]],
+    border_prefiltered: bool,
+) -> bool:
+    """Detect and remove page-border decoration strips.
+
+    Returns updated border_prefiltered flag.
+    """
+    border_strip_removed = 0
+    if border_prefiltered:
+        logger.info("Step 4e: skipped (border pre-filter already applied)")
+        return border_prefiltered
+
+    for z in zones_data:
+        cells = z.get("cells", [])
+        if not cells:
+            continue
+        all_wbs_with_cell: list = []
+        for cell in cells:
+            for wb in cell.get("word_boxes") or []:
+                all_wbs_with_cell.append((wb.get("left", 0), wb, cell))
+        if len(all_wbs_with_cell) < 10:
+            continue
+        all_wbs_with_cell.sort(key=lambda t: t[0])
+        total = len(all_wbs_with_cell)
+
+        # -- Left-edge scan --
+        left_strip_count = 0
+        left_gap = 0
+        running_right = 0
+        for gi in range(total - 1):
+            running_right = max(
+                running_right,
+                all_wbs_with_cell[gi][0] + all_wbs_with_cell[gi][1].get("width", 0),
+            )
+            gap = all_wbs_with_cell[gi + 1][0] - running_right
+            if gap > 30:
+                left_strip_count = gi + 1
+                left_gap = gap
+                break
+
+        # -- Right-edge scan --
+        right_strip_count = 0
+        right_gap = 0
+        running_left = all_wbs_with_cell[-1][0]
+        for gi in range(total - 1, 0, -1):
+            running_left = min(running_left, all_wbs_with_cell[gi][0])
+            prev_right = (
+                all_wbs_with_cell[gi - 1][0]
+                + all_wbs_with_cell[gi - 1][1].get("width", 0)
+            )
+            gap = running_left - prev_right
+            if gap > 30:
+                right_strip_count = total - gi
+                right_gap = gap
+                break
+
+        strip_wbs: set = set()
+        strip_side = ""
+        strip_gap = 0
+        strip_count = 0
+        if left_strip_count > 0 and left_strip_count / total < 0.20:
+            strip_side = "left"
+            strip_count = left_strip_count
+            strip_gap = left_gap
+            strip_wbs = {id(t[1]) for t in all_wbs_with_cell[:left_strip_count]}
+        elif right_strip_count > 0 and right_strip_count / total < 0.20:
+            strip_side = "right"
+            strip_count = right_strip_count
+            strip_gap = right_gap
+            strip_wbs = {id(t[1]) for t in all_wbs_with_cell[total - right_strip_count:]}
+
+        if not strip_wbs:
+            continue
+        for cell in cells:
+            wbs = cell.get("word_boxes") or []
+            filtered = [wb for wb in wbs if id(wb) not in strip_wbs]
+            if len(filtered) < len(wbs):
+                border_strip_removed += len(wbs) - len(filtered)
+                cell["word_boxes"] = filtered
+                cell["text"] = _words_to_reading_order_text(filtered)
+        z["cells"] = [c for c in cells
+                      if (c.get("word_boxes") or c.get("text", "").strip())]
+        logger.info(
+            "Step 4e: removed %d border-strip word_boxes (%s) from zone %d "
+            "(gap=%dpx, strip=%d/%d wbs)",
+            border_strip_removed, strip_side, z.get("zone_index", 0),
+            strip_gap, strip_count, total,
+        )
+
+    return border_prefiltered
+
+
+def _remove_alphabet_sidebars(zones_data: List[Dict[str, Any]]) -> None:
+    """Remove decorative edge columns (alphabet sidebar safety net).
+
+    Dictionary pages have A-Z letter sidebars that OCR reads as single-
+    character word_boxes.
+    """
+    for z in zones_data:
+        columns = z.get("columns", [])
+        cells = z.get("cells", [])
+        if len(columns) < 3 or not cells:
+            continue
+        col_cells: Dict[str, List[Dict]] = {}
+        for cell in cells:
+            ct = cell.get("col_type", "")
+            if ct.startswith("column_"):
+                col_cells.setdefault(ct, []).append(cell)
+        col_types_ordered = sorted(col_cells.keys())
+        if len(col_types_ordered) < 3:
+            continue
+        for edge_ct in [col_types_ordered[0], col_types_ordered[-1]]:
+            edge_cells_list = col_cells.get(edge_ct, [])
+            if len(edge_cells_list) < 3:
+                continue
+            texts = [(c.get("text") or "").strip() for c in edge_cells_list]
+            avg_len = sum(len(t) for t in texts) / len(texts)
+            single_char = sum(1 for t in texts if len(t) <= 1)
+            single_ratio = single_char / len(texts)
+            if avg_len > 1.5:
+                continue
+            if single_ratio < 0.7:
+                continue
+            removed_count = len(edge_cells_list)
+            edge_ids = {id(c) for c in edge_cells_list}
+            z["cells"] = [c for c in cells if id(c) not in edge_ids]
+            z["columns"] = [col for col in columns if col.get("col_type") != edge_ct]
+            logger.info(
+                "Step 4f: removed decorative edge column '%s' from zone %d "
+                "(%d cells, avg_len=%.1f, single_char=%.0f%%)",
+                edge_ct, z.get("zone_index", 0), removed_count,
+                avg_len, single_ratio * 100,
+            )
+            break  # only remove one edge per zone

klausur-service/backend/grid/build/core.py

@@ -0,0 +1,213 @@
+"""
+Grid Build Core — the main _build_grid_core() function.
+
+Extracted from grid_editor_api.py for maintainability.
+Takes merged OCR word positions and builds a structured, zone-aware grid.
+
+The function delegates to phase-specific modules:
+- grid_build_zones.py   — image loading, graphic/box detection, zone grids
+- grid_build_cleanup.py — junk rows, artifacts, pipes, border strips
+- grid_build_text_ops.py — color, headings, IPA, page refs
+- grid_build_finalize.py — bullets, max_columns, dictionary, spelling, result
+"""
+
+import logging
+import time
+from typing import Any, Dict, List, Optional
+
+from grid.editor.filters import (
+    _flatten_word_boxes,
+    _get_content_bounds,
+    _filter_decorative_margin,
+    _filter_footer_words,
+    _filter_header_junk,
+)
+
+from .zones import _build_zones
+from .cleanup import _cleanup_zones
+from .text_ops import _process_text
+from .finalize import _finalize_grid
+
+logger = logging.getLogger(__name__)
+
+
+async def _build_grid_core(
+    session_id: str,
+    session: dict,
+    *,
+    ipa_mode: str = "auto",
+    syllable_mode: str = "auto",
+    enhance: bool = True,
+    max_columns: Optional[int] = None,
+    min_conf: Optional[int] = None,
+) -> dict:
+    """Core grid building logic — pure computation, no HTTP or DB side effects.
+
+    Args:
+        session_id: Session identifier (for logging and image loading).
+        session: Full session dict from get_session_db().
+        ipa_mode: "auto" (only when English headwords detected), "all"
+            (force IPA on all content columns), "en" (English column only),
+            "de" (German/definition columns only), or "none" (skip entirely).
+        syllable_mode: "auto" (only when original has pipe dividers),
+            "all" (force syllabification on all words), "en" (English only),
+            "de" (German only), or "none" (skip).
+
+    Returns:
+        StructuredGrid result dict.
+
+    Raises:
+        ValueError: If session data is incomplete.
+    """
+    t0 = time.time()
+
+    # ── Phase 1: Input Validation & Word Filtering ──────────────────
+
+    # 1. Validate and load word results
+    word_result = session.get("word_result")
+    if not word_result or not word_result.get("cells"):
+        raise ValueError("No word results found. Run paddle-kombi or rapid-kombi first.")
+
+    img_w = word_result.get("image_width", 0)
+    img_h = word_result.get("image_height", 0)
+    if not img_w or not img_h:
+        raise ValueError("Missing image dimensions in word_result")
+
+    # 2. Flatten all word boxes from cells
+    all_words = _flatten_word_boxes(word_result["cells"])
+    if not all_words:
+        raise ValueError("No word boxes found in cells")
+
+    # 2a-pre. Apply min_conf filter if specified
+    if min_conf and min_conf > 0:
+        before = len(all_words)
+        all_words = [w for w in all_words if w.get('conf', 100) >= min_conf]
+        removed = before - len(all_words)
+        if removed:
+            logger.info("build-grid session %s: min_conf=%d removed %d/%d words",
+                        session_id, min_conf, removed, before)
+
+    logger.info("build-grid session %s: %d words from %d cells (enhance=%s, max_cols=%s, min_conf=%s)",
+                session_id, len(all_words), len(word_result["cells"]),
+                enhance, max_columns, min_conf)
+
+    # 2b. Filter decorative margin columns (alphabet graphics)
+    margin_strip_info = _filter_decorative_margin(all_words, img_w, logger, session_id)
+    margin_strip_detected = margin_strip_info.get("found", False)
+
+    # Read document_category from session
+    document_category = session.get("document_category")
+
+    # 2c. Filter footer rows (page numbers at the very bottom)
+    page_number_info = _filter_footer_words(all_words, img_h, logger, session_id)
+
+    # 2c2. Filter OCR junk from header illustrations
+    _filter_header_junk(all_words, img_h, logger, session_id)
+
+    # 2d. Filter words inside user-defined exclude regions
+    structure_result = session.get("structure_result")
+    exclude_rects = []
+    if structure_result:
+        for er in structure_result.get("exclude_regions", []):
+            exclude_rects.append({
+                "x": er["x"], "y": er["y"],
+                "w": er["w"], "h": er["h"],
+            })
+    if exclude_rects:
+        before = len(all_words)
+        filtered = []
+        for w in all_words:
+            w_cx = w["left"] + w.get("width", 0) / 2
+            w_cy = w["top"] + w.get("height", 0) / 2
+            inside = any(
+                er["x"] <= w_cx <= er["x"] + er["w"]
+                and er["y"] <= w_cy <= er["y"] + er["h"]
+                for er in exclude_rects
+            )
+            if not inside:
+                filtered.append(w)
+        removed = before - len(filtered)
+        if removed:
+            all_words = filtered
+            logger.info(
+                "build-grid session %s: removed %d words inside %d user exclude region(s)",
+                session_id, removed, len(exclude_rects),
+            )
+
+    # 2e. Hard-filter words inside graphic/image regions from structure step
+    graphic_rects: List[Dict[str, int]] = []
+    if structure_result:
+        for g in structure_result.get("graphics", []):
+            graphic_rects.append({
+                "x": g["x"], "y": g["y"],
+                "w": g["w"], "h": g["h"],
+            })
+    if graphic_rects:
+        before = len(all_words)
+        all_words = [
+            w for w in all_words
+            if not any(
+                gr["x"] <= w["left"] + w.get("width", 0) / 2 <= gr["x"] + gr["w"]
+                and gr["y"] <= w["top"] + w.get("height", 0) / 2 <= gr["y"] + gr["h"]
+                for gr in graphic_rects
+            )
+        ]
+        removed = before - len(all_words)
+        if removed:
+            logger.info(
+                "build-grid session %s: hard-removed %d words inside %d structure graphic region(s)",
+                session_id, removed, len(graphic_rects),
+            )
+
+    content_x, content_y, content_w, content_h = _get_content_bounds(all_words)
+
+    # ── Phase 2: Image Processing & Zone Detection ──────────────────
+
+    zone_result = await _build_zones(
+        session_id, session, all_words, graphic_rects,
+        content_x, content_y, content_w, content_h,
+        img_w, img_h,
+    )
+    zones_data = zone_result["zones_data"]
+    boxes_detected = zone_result["boxes_detected"]
+    recovered_count = zone_result["recovered_count"]
+    border_prefiltered = zone_result["border_prefiltered"]
+    img_bgr = zone_result["img_bgr"]
+
+    # ── Phase 3: Junk Removal & Cell Cleanup ────────────────────────
+
+    border_prefiltered = _cleanup_zones(zones_data, border_prefiltered, session_id)
+
+    # ── Phase 4+5a: Color, Headings, IPA, Page Refs ─────────────────
+
+    text_result = _process_text(
+        zones_data, img_bgr, img_w, img_h, ipa_mode, page_number_info,
+    )
+
+    # ── Phase 5b+6: Finalize & Result Assembly ──────────────────────
+
+    duration = time.time() - t0
+
+    result = _finalize_grid(
+        zones_data=zones_data,
+        all_words=all_words,
+        img_bgr=img_bgr,
+        img_w=img_w,
+        img_h=img_h,
+        session_id=session_id,
+        max_columns=max_columns,
+        ipa_mode=ipa_mode,
+        syllable_mode=syllable_mode,
+        en_col_type=text_result["en_col_type"],
+        ipa_target_cols=text_result["ipa_target_cols"],
+        all_content_cols=text_result["all_content_cols"],
+        skip_ipa=text_result["skip_ipa"],
+        document_category=document_category,
+        margin_strip_detected=margin_strip_detected,
+        page_number_info=text_result["page_number_info"],
+        boxes_detected=boxes_detected,
+        recovered_count=recovered_count,
+        duration=duration,
+    )
+
+    return result

klausur-service/backend/grid/build/finalize.py

@@ -0,0 +1,452 @@
+"""
+Grid Build Finalize — Phase 5b+6: Orchestrates cell-level operations,
+dictionary detection, syllable dividers, spell checking, empty column
+removal, and result assembly.
+
+Extracted from grid_build_core.py for maintainability.
+"""
+
+import logging
+import re
+from typing import Any, Dict, List, Optional
+
+from .cell_ops import (
+    _remove_bullets_and_artifacts,
+    _remove_garbled_cells,
+    _normalize_word_order,
+    _enforce_max_columns,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _finalize_grid(
+    zones_data: List[Dict[str, Any]],
+    all_words: List[Dict[str, Any]],
+    img_bgr: Any,
+    img_w: int,
+    img_h: int,
+    session_id: str,
+    max_columns: Optional[int],
+    ipa_mode: str,
+    syllable_mode: str,
+    en_col_type: Optional[str],
+    ipa_target_cols: set,
+    all_content_cols: set,
+    skip_ipa: bool,
+    document_category: Optional[str],
+    margin_strip_detected: bool,
+    page_number_info: Optional[Dict],
+    boxes_detected: int,
+    recovered_count: int,
+    duration: float,
+) -> dict:
+    """Run final processing steps and assemble result dict.
+
+    Handles: bullet removal, artifact cells, word ordering, max_columns,
+    dictionary detection, syllable dividers, spell check, empty columns,
+    internal flag cleanup, and result assembly.
+    """
+    total_cols = sum(len(z.get("columns", [])) for z in zones_data)
+
+    # 5i. Remove blue bullet/artifact word_boxes
+    _remove_bullets_and_artifacts(zones_data)
+
+    # 5j-pre. Remove cells whose text is entirely garbled / artifact noise
+    _remove_garbled_cells(zones_data)
+
+    # 5j. Normalise word_box order to reading order
+    _normalize_word_order(zones_data)
+
+    # 5k. Enforce max_columns by merging narrowest columns
+    if max_columns and max_columns > 0:
+        _enforce_max_columns(zones_data, max_columns)
+
+    # --- Dictionary detection on assembled grid ---
+    dict_detection = _detect_dictionary(
+        zones_data, img_w, img_h, document_category, margin_strip_detected
+    )
+
+    # --- Word-gap merge ---
+    try:
+        from cv_syllable_detect import merge_word_gaps_in_zones
+        merge_word_gaps_in_zones(zones_data, session_id)
+    except Exception as e:
+        logger.warning("Word-gap merge failed: %s", e)
+
+    # --- Pipe auto-correction ---
+    try:
+        from cv_syllable_detect import autocorrect_pipe_artifacts
+        autocorrect_pipe_artifacts(zones_data, session_id)
+    except Exception as e:
+        logger.warning("Pipe autocorrect failed: %s", e)
+
+    # --- Syllable divider insertion ---
+    syllable_insertions = _insert_syllable_dividers(
+        zones_data, img_bgr, session_id, syllable_mode, dict_detection,
+        en_col_type, all_content_cols, total_cols,
+    )
+
+    # --- Split merged words ---
+    _split_merged_words(zones_data, session_id)
+
+    # --- Ensure space before IPA/phonetic brackets ---
+    _fix_ipa_spacing(zones_data)
+
+    # --- SmartSpellChecker ---
+    _run_spell_checker(zones_data, session_id, en_col_type, total_cols)
+
+    # --- Debug log cell counts per column ---
+    for z in zones_data:
+        if z.get("zone_type") == "content":
+            from collections import Counter as _Counter
+            _cc = _Counter(c.get("col_index") for c in z.get("cells", []))
+            _cols = z.get("columns", [])
+            logger.info(
+                "pre-empty-col-removal zone %d: %d cols, cells_per_col=%s",
+                z.get("zone_index", 0), len(_cols), dict(sorted(_cc.items())),
+            )
+
+    # --- Remove empty columns ---
+    _remove_empty_columns(zones_data)
+
+    # Clean up internal flags before returning
+    for z in zones_data:
+        for cell in z.get("cells", []):
+            cell.pop("_ipa_corrected", None)
+
+    # 6. Build result
+    return _assemble_result(
+        zones_data, all_words, img_w, img_h, session_id,
+        ipa_mode, syllable_mode, ipa_target_cols, skip_ipa,
+        dict_detection, page_number_info, boxes_detected,
+        recovered_count, duration, syllable_insertions,
+    )
+
+
+def _detect_dictionary(
+    zones_data: List[Dict[str, Any]],
+    img_w: int,
+    img_h: int,
+    document_category: Optional[str],
+    margin_strip_detected: bool,
+) -> Dict[str, Any]:
+    """Run dictionary detection on the assembled grid."""
+    from cv_layout import _score_dictionary_signals
+    dict_detection: Dict[str, Any] = {"is_dictionary": False, "confidence": 0.0}
+    try:
+        from cv_vocab_types import ColumnGeometry
+        for z in zones_data:
+            zone_cells = z.get("cells", [])
+            zone_cols = z.get("columns", [])
+            if len(zone_cols) < 2 or len(zone_cells) < 10:
+                continue
+            pseudo_geoms = []
+            for col in zone_cols:
+                ci = col["index"]
+                col_cells = [c for c in zone_cells if c.get("col_index") == ci]
+                col_words = []
+                for cell in col_cells:
+                    for wb in cell.get("word_boxes") or []:
+                        col_words.append({
+                            "text": wb.get("text", ""),
+                            "conf": wb.get("conf", 0),
+                            "top": wb.get("top", 0),
+                            "left": wb.get("left", 0),
+                            "height": wb.get("height", 0),
+                            "width": wb.get("width", 0),
+                        })
+                    if not cell.get("word_boxes") and cell.get("text"):
+                        col_words.append({
+                            "text": cell["text"],
+                            "conf": cell.get("confidence", 50),
+                            "top": cell.get("bbox_px", {}).get("y", 0),
+                            "left": cell.get("bbox_px", {}).get("x", 0),
+                            "height": cell.get("bbox_px", {}).get("h", 20),
+                            "width": cell.get("bbox_px", {}).get("w", 50),
+                        })
+                col_w = col.get("x_max_px", 0) - col.get("x_min_px", 0)
+                pseudo_geoms.append(ColumnGeometry(
+                    index=ci, x=col.get("x_min_px", 0), y=0,
+                    width=max(col_w, 1), height=img_h,
+                    word_count=len(col_words), words=col_words,
+                    width_ratio=col_w / max(img_w, 1),
+                ))
+            if len(pseudo_geoms) >= 2:
+                dd = _score_dictionary_signals(
+                    pseudo_geoms,
+                    document_category=document_category,
+                    margin_strip_detected=margin_strip_detected,
+                )
+                if dd["confidence"] > dict_detection["confidence"]:
+                    dict_detection = dd
+    except Exception as e:
+        logger.warning("Dictionary detection failed: %s", e)
+    return dict_detection
+
+
+def _insert_syllable_dividers(
+    zones_data: List[Dict[str, Any]],
+    img_bgr: Any,
+    session_id: str,
+    syllable_mode: str,
+    dict_detection: Dict[str, Any],
+    en_col_type: Optional[str],
+    all_content_cols: set,
+    total_cols: int,
+) -> int:
+    """Insert syllable dividers for dictionary pages. Returns insertion count."""
+    syllable_insertions = 0
+    if syllable_mode == "none" or img_bgr is None:
+        if syllable_mode == "none":
+            for z in zones_data:
+                for cell in z.get("cells", []):
+                    t = cell.get("text", "")
+                    if "|" in t:
+                        cell["text"] = t.replace("|", "")
+        return syllable_insertions
+
+    _syllable_eligible = False
+    if syllable_mode in ("all", "de", "en"):
+        _syllable_eligible = True
+    elif (dict_detection.get("is_dictionary")
+            and dict_detection.get("article_col_index") is not None):
+        _syllable_eligible = True
+
+    _syllable_col_filter: Optional[set] = None
+    if syllable_mode == "en":
+        _syllable_col_filter = {en_col_type} if en_col_type else set()
+    elif syllable_mode == "de":
+        if en_col_type and total_cols >= 3:
+            _syllable_col_filter = all_content_cols - {en_col_type}
+
+    if _syllable_eligible:
+        try:
+            from cv_syllable_detect import insert_syllable_dividers
+            force_syllables = (syllable_mode in ("all", "de", "en"))
+            syllable_insertions = insert_syllable_dividers(
+                zones_data, img_bgr, session_id,
+                force=force_syllables,
+                col_filter=_syllable_col_filter,
+            )
+        except Exception as e:
+            logger.warning("Syllable insertion failed: %s", e)
+
+    return syllable_insertions
+
+
+def _split_merged_words(
+    zones_data: List[Dict[str, Any]],
+    session_id: str,
+) -> None:
+    """Split merged words using dictionary lookup."""
+    try:
+        from cv_review import _try_split_merged_word, _SPELL_AVAILABLE
+        if not _SPELL_AVAILABLE:
+            return
+        split_count = 0
+        for z in zones_data:
+            for cell in z.get("cells", []):
+                text = cell.get("text", "")
+                if not text:
+                    continue
+                parts = []
+                changed = False
+                for token in text.split():
+                    clean = token
+                    bracket_pos = clean.find('[')
+                    suffix_ipa = ""
+                    if bracket_pos > 0:
+                        suffix_ipa = clean[bracket_pos:]
+                        clean = clean[:bracket_pos]
+                    suffix_punct = ""
+                    stripped = clean.rstrip(".,!?;:'\")")
+                    if stripped != clean:
+                        suffix_punct = clean[len(stripped):]
+                        clean = stripped
+                    suffix = suffix_punct + suffix_ipa
+                    contraction = ""
+                    if "'" in clean and clean.index("'") >= 2:
+                        apos_pos = clean.index("'")
+                        contraction = clean[apos_pos:]
+                        clean = clean[:apos_pos]
+                        suffix = contraction + suffix
+                    if len(clean) >= 4 and clean.isalpha():
+                        split = _try_split_merged_word(clean)
+                        if split:
+                            parts.append(split + suffix)
+                            changed = True
+                            continue
+                    parts.append(token)
+                if changed:
+                    cell["text"] = " ".join(parts)
+                    split_count += 1
+        if split_count:
+            logger.info("build-grid session %s: split %d merged words", session_id, split_count)
+    except ImportError:
+        pass
+
+
+def _fix_ipa_spacing(zones_data: List[Dict[str, Any]]) -> None:
+    """Ensure space before IPA/phonetic brackets: 'word[ipa]' -> 'word [ipa]'."""
+    _IPA_NOSPACE_RE = re.compile(r'([a-zA-ZäöüÄÖÜß])(\[[^\]]{2,}\])')
+    for z in zones_data:
+        for cell in z.get("cells", []):
+            text = cell.get("text", "")
+            if text and "[" in text:
+                fixed = _IPA_NOSPACE_RE.sub(r'\1 \2', text)
+                if fixed != text:
+                    cell["text"] = fixed
+
+
+def _run_spell_checker(
+    zones_data: List[Dict[str, Any]],
+    session_id: str,
+    en_col_type: Optional[str],
+    total_cols: int,
+) -> None:
+    """Run SmartSpellChecker on all cells."""
+    try:
+        from smart_spell import SmartSpellChecker
+        _ssc = SmartSpellChecker()
+        spell_fix_count = 0
+
+        for z in zones_data:
+            for cell in z.get("cells", []):
+                text = cell.get("text", "")
+                if not text or not text.strip():
+                    continue
+                ct = cell.get("col_type", "")
+                if not ct.startswith("column_"):
+                    continue
+
+                if total_cols >= 3 and en_col_type:
+                    lang = "en" if ct == en_col_type else "de"
+                elif total_cols <= 2:
+                    lang = "auto"
+                else:
+                    lang = "auto"
+
+                result = _ssc.correct_text(text, lang=lang)
+                if result.changed:
+                    cell["text"] = result.corrected
+                    spell_fix_count += 1
+
+        if spell_fix_count:
+            logger.info(
+                "build-grid session %s: SmartSpellChecker fixed %d cells",
+                session_id, spell_fix_count,
+            )
+    except ImportError:
+        logger.debug("SmartSpellChecker not available in build-grid")
+    except Exception as e:
+        logger.warning("SmartSpellChecker error in build-grid: %s", e)
+
+
+def _remove_empty_columns(zones_data: List[Dict[str, Any]]) -> None:
+    """Remove columns that have no cells assigned."""
+    for z in zones_data:
+        cells = z.get("cells", [])
+        used_col_indices = {c.get("col_index") for c in cells}
+        old_cols = z.get("columns", [])
+        new_cols = [c for c in old_cols if c.get("col_index", c.get("index", -1)) in used_col_indices]
+        if len(new_cols) < len(old_cols):
+            old_to_new = {}
+            for new_i, col in enumerate(new_cols):
+                old_i = col.get("col_index", col.get("index", new_i))
+                old_to_new[old_i] = new_i
+                col["col_index"] = new_i
+                col["index"] = new_i
+                col["label"] = f"column_{new_i + 1}" if len(new_cols) > 1 else "column_text"
+            for cell in cells:
+                old_ci = cell.get("col_index", 0)
+                cell["col_index"] = old_to_new.get(old_ci, old_ci)
+                cell["col_type"] = f"column_{cell['col_index'] + 1}" if len(new_cols) > 1 else "column_text"
+            z["columns"] = new_cols
+
+
+def _assemble_result(
+    zones_data: List[Dict[str, Any]],
+    all_words: List[Dict[str, Any]],
+    img_w: int,
+    img_h: int,
+    session_id: str,
+    ipa_mode: str,
+    syllable_mode: str,
+    ipa_target_cols: set,
+    skip_ipa: bool,
+    dict_detection: Dict[str, Any],
+    page_number_info: Optional[Dict],
+    boxes_detected: int,
+    recovered_count: int,
+    duration: float,
+    syllable_insertions: int,
+) -> dict:
+    """Build the final result dict (Phase 6)."""
+    total_cells = sum(len(z.get("cells", [])) for z in zones_data)
+    total_columns = sum(len(z.get("columns", [])) for z in zones_data)
+    total_rows = sum(len(z.get("rows", [])) for z in zones_data)
+
+    # Collect color statistics
+    color_stats: Dict[str, int] = {}
+    for z in zones_data:
+        for cell in z.get("cells", []):
+            for wb in cell.get("word_boxes", []):
+                cn = wb.get("color_name", "black")
+                color_stats[cn] = color_stats.get(cn, 0) + 1
+
+    # Compute layout metrics
+    all_content_row_heights: List[float] = []
+    for z in zones_data:
+        for row in z.get("rows", []):
+            if not row.get("is_header", False):
+                h = row.get("y_max_px", 0) - row.get("y_min_px", 0)
+                if h > 0:
+                    all_content_row_heights.append(h)
+    avg_row_height = (
+        sum(all_content_row_heights) / len(all_content_row_heights)
+        if all_content_row_heights else 30.0
+    )
+    font_size_suggestion = max(10, int(avg_row_height * 0.6))
+
+    return {
+        "session_id": session_id,
+        "image_width": img_w,
+        "image_height": img_h,
+        "zones": zones_data,
+        "boxes_detected": boxes_detected,
+        "summary": {
+            "total_zones": len(zones_data),
+            "total_columns": total_columns,
+            "total_rows": total_rows,
+            "total_cells": total_cells,
+            "total_words": len(all_words),
+            "recovered_colored": recovered_count,
+            "color_stats": color_stats,
+        },
+        "formatting": {
+            "bold_columns": [],
+            "header_rows": [],
+        },
+        "layout_metrics": {
+            "page_width_px": img_w,
+            "page_height_px": img_h,
+            "avg_row_height_px": round(avg_row_height, 1),
+            "font_size_suggestion_px": font_size_suggestion,
+        },
+        "dictionary_detection": {
+            "is_dictionary": dict_detection.get("is_dictionary", False),
+            "confidence": dict_detection.get("confidence", 0.0),
+            "signals": dict_detection.get("signals", {}),
+            "article_col_index": dict_detection.get("article_col_index"),
+            "headword_col_index": dict_detection.get("headword_col_index"),
+        },
+        "processing_modes": {
+            "ipa_mode": ipa_mode,
+            "syllable_mode": syllable_mode,
+            "ipa_applied": bool(ipa_target_cols) if not skip_ipa else False,
+            "syllables_applied": syllable_insertions > 0,
+        },
+        "page_number": page_number_info,
+        "duration_seconds": round(duration, 2),
+    }

klausur-service/backend/grid/build/text_ops.py

@@ -0,0 +1,489 @@
+"""
+Grid Build Text Ops — Phase 4+5a: Color annotation, heading detection,
+parenthesis fix, IPA phonetic correction, page ref extraction, and
+slash-IPA conversion.
+
+Extracted from grid_build_core.py for maintainability.
+"""
+
+import logging
+import re
+from typing import Any, Dict, List, Optional, Set, Tuple
+
+from cv_color_detect import detect_word_colors
+from cv_ocr_engines import (
+    fix_cell_phonetics, fix_ipa_continuation_cell, _text_has_garbled_ipa,
+    _lookup_ipa,
+)
+from grid.editor.headers import (
+    _detect_heading_rows_by_color,
+    _detect_heading_rows_by_single_cell,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _process_text(
+    zones_data: List[Dict[str, Any]],
+    img_bgr: Any,
+    img_w: int,
+    img_h: int,
+    ipa_mode: str,
+    page_number_info: Optional[Dict],
+) -> Dict[str, Any]:
+    """Run color annotation, heading detection, IPA correction, and page refs.
+
+    Args:
+        zones_data: List of zone dicts (modified in place).
+        img_bgr: BGR image array (or None).
+        img_w: Image width.
+        img_h: Image height.
+        ipa_mode: IPA processing mode.
+        page_number_info: Existing page number metadata (may be None).
+
+    Returns:
+        Dict with keys: en_col_type, ipa_target_cols, all_content_cols,
+        skip_ipa, page_number_info.
+    """
+    # 5. Color annotation on final word_boxes in cells
+    if img_bgr is not None:
+        all_wb: List[Dict] = []
+        for z in zones_data:
+            for cell in z.get("cells", []):
+                all_wb.extend(cell.get("word_boxes", []))
+        detect_word_colors(img_bgr, all_wb)
+
+    # 5a. Heading detection by color + height
+    heading_count = _detect_heading_rows_by_color(zones_data, img_w, img_h)
+    if heading_count:
+        logger.info("Detected %d heading rows by color+height", heading_count)
+
+    # 5b. Fix unmatched parentheses in cell text
+    for z in zones_data:
+        for cell in z.get("cells", []):
+            text = cell.get("text", "")
+            if ")" in text and "(" not in text:
+                cell["text"] = "(" + text
+
+    # 5c. IPA phonetic correction
+    all_cells = [cell for z in zones_data for cell in z.get("cells", [])]
+    total_cols = sum(len(z.get("columns", [])) for z in zones_data)
+    en_col_type = None
+    ipa_target_cols: set = set()
+    all_content_cols: set = set()
+    skip_ipa = (ipa_mode == "none")
+
+    # When ipa_mode=none, strip ALL square brackets from ALL content columns
+    if skip_ipa:
+        _SQUARE_BRACKET_RE_NONE = re.compile(r'\s*\[[^\]]+\]')
+        for cell in all_cells:
+            ct = cell.get("col_type", "")
+            if not ct.startswith("column_"):
+                continue
+            text = cell.get("text", "")
+            if "[" in text:
+                stripped = _SQUARE_BRACKET_RE_NONE.sub("", text)
+                if stripped != text:
+                    cell["text"] = stripped.strip()
+                    cell["_ipa_corrected"] = True
+
+    if not skip_ipa and total_cols >= 3:
+        en_col_type, ipa_target_cols, all_content_cols = _run_ipa_correction(
+            all_cells, total_cols, ipa_mode, zones_data
+        )
+    elif not skip_ipa:
+        # Collect all_content_cols even when <3 cols (needed by finalize)
+        for cell in all_cells:
+            ct = cell.get("col_type", "")
+            if ct.startswith("column_") and (cell.get("text") or "").strip():
+                all_content_cols.add(ct)
+
+    # 5e. Heading detection by single-cell rows
+    single_heading_count = _detect_heading_rows_by_single_cell(zones_data, img_w, img_h)
+    if single_heading_count:
+        logger.info("Detected %d heading rows by single-cell heuristic", single_heading_count)
+
+    # 5f. Strip IPA from headings
+    for z in zones_data:
+        for cell in z.get("cells", []):
+            if cell.get("col_type") != "heading":
+                continue
+            text = cell.get("text", "")
+            stripped = re.sub(r'\s*\[[^\]]*\]\s*$', '', text).strip()
+            if stripped and stripped != text:
+                cell["text"] = stripped
+
+    # 5g. Extract page_ref cells and footer rows
+    _extract_page_refs_and_footers(zones_data, page_number_info)
+
+    # 5h. Convert slash-delimited IPA to bracket notation
+    _convert_slash_ipa(zones_data, skip_ipa, en_col_type)
+
+    return {
+        "en_col_type": en_col_type,
+        "ipa_target_cols": ipa_target_cols,
+        "all_content_cols": all_content_cols,
+        "skip_ipa": skip_ipa,
+        "page_number_info": page_number_info,
+    }
+
+
+def _run_ipa_correction(
+    all_cells: List[Dict],
+    total_cols: int,
+    ipa_mode: str,
+    zones_data: List[Dict[str, Any]],
+) -> Tuple[Optional[str], set, set]:
+    """Run IPA correction on cells. Returns (en_col_type, ipa_target_cols, all_content_cols)."""
+    en_col_type = None
+    all_content_cols: set = set()
+
+    # Detect English headword column via IPA signals
+    col_ipa_count: Dict[str, int] = {}
+    for cell in all_cells:
+        ct = cell.get("col_type", "")
+        if not ct.startswith("column_"):
+            continue
+        txt = cell.get("text", "") or ""
+        if txt.strip():
+            all_content_cols.add(ct)
+        if '[' in txt or _text_has_garbled_ipa(txt):
+            col_ipa_count[ct] = col_ipa_count.get(ct, 0) + 1
+    if col_ipa_count:
+        en_col_type = max(col_ipa_count, key=col_ipa_count.get)
+    elif ipa_mode == "all":
+        col_cell_count: Dict[str, int] = {}
+        for cell in all_cells:
+            ct = cell.get("col_type", "")
+            if ct.startswith("column_") and (cell.get("text") or "").strip():
+                col_cell_count[ct] = col_cell_count.get(ct, 0) + 1
+        if col_cell_count:
+            en_col_type = max(col_cell_count, key=col_cell_count.get)
+
+    # Decide which columns to process based on ipa_mode
+    en_ipa_target_cols: set = set()
+    de_ipa_target_cols: set = set()
+    if ipa_mode in ("auto", "en"):
+        if en_col_type:
+            en_ipa_target_cols.add(en_col_type)
+    elif ipa_mode == "de":
+        de_ipa_target_cols = all_content_cols - {en_col_type} if en_col_type else all_content_cols
+    elif ipa_mode == "all":
+        if en_col_type:
+            en_ipa_target_cols.add(en_col_type)
+        de_ipa_target_cols = all_content_cols - en_ipa_target_cols
+
+    # --- Strip IPA from columns NOT in the target set ---
+    _SQUARE_BRACKET_RE = re.compile(r'\s*\[[^\]]+\]')
+    strip_en_ipa = en_col_type and en_col_type not in en_ipa_target_cols
+    if strip_en_ipa or ipa_mode == "none":
+        strip_cols = {en_col_type} if strip_en_ipa and ipa_mode != "none" else all_content_cols
+        for cell in all_cells:
+            ct = cell.get("col_type", "")
+            if ct not in strip_cols:
+                continue
+            text = cell.get("text", "")
+            if "[" in text:
+                stripped = _SQUARE_BRACKET_RE.sub("", text)
+                if stripped != text:
+                    cell["text"] = stripped.strip()
+                    cell["_ipa_corrected"] = True
+
+    # --- English IPA (Britfone + eng_to_ipa) ---
+    if en_ipa_target_cols:
+        for cell in all_cells:
+            ct = cell.get("col_type")
+            if ct in en_ipa_target_cols:
+                cell["_orig_col_type"] = ct
+                cell["col_type"] = "column_en"
+    _pre_ipa = {id(c): c.get("text", "") for c in all_cells}
+    fix_cell_phonetics(all_cells, pronunciation="british")
+    for cell in all_cells:
+        orig = cell.pop("_orig_col_type", None)
+        if orig:
+            cell["col_type"] = orig
+        if cell.get("text", "") != _pre_ipa.get(id(cell), ""):
+            cell["_ipa_corrected"] = True
+
+    # --- German IPA (wiki-pronunciation-dict + epitran) ---
+    if de_ipa_target_cols:
+        from cv_ipa_german import insert_german_ipa
+        insert_german_ipa(all_cells, de_ipa_target_cols)
+
+    ipa_target_cols = en_ipa_target_cols | de_ipa_target_cols
+
+    # Mark cells whose text was changed by IPA correction
+    for cell in all_cells:
+        if cell.get("text", "") != _pre_ipa.get(id(cell), ""):
+            cell["_ipa_corrected"] = True
+
+    # 5d. Fix IPA continuation cells
+    skip_ipa = (ipa_mode == "none")
+    _REAL_IPA_CHARS = set("ˈˌəɪɛɒʊʌæɑɔʃʒθðŋ")
+    ipa_cont_fixed = 0
+    for z in ([] if skip_ipa else zones_data):
+        rows_sorted = sorted(z.get("rows", []), key=lambda r: r["index"])
+        z_cells = z.get("cells", [])
+        for idx, row in enumerate(rows_sorted):
+            if idx == 0:
+                continue
+            ri = row["index"]
+            row_cells = [c for c in z_cells if c.get("row_index") == ri]
+            for cell in row_cells:
+                ct = cell.get("col_type", "")
+                if not ct.startswith("column_"):
+                    continue
+                cell_text = (cell.get("text") or "").strip()
+                if not cell_text:
+                    wb_texts = [w.get("text", "")
+                                for w in cell.get("word_boxes", [])]
+                    cell_text = " ".join(wb_texts).strip()
+                    if not cell_text:
+                        continue
+
+                is_bracketed = (
+                    cell_text.startswith('[') and cell_text.endswith(']')
+                )
+
+                if is_bracketed:
+                    if not _text_has_garbled_ipa(cell_text):
+                        continue
+                    if re.search(r'\[[^\]]*[ˈˌəɪɛɒʊʌæɑɔʃʒθðŋ][^\]]*\]', cell_text):
+                        continue
+                else:
+                    content_cells_in_row = [
+                        c for c in row_cells
+                        if c.get("col_type", "").startswith("column_")
+                        and c.get("col_type") != "column_1"
+                    ]
+                    if len(content_cells_in_row) != 1:
+                        continue
+                    if not _text_has_garbled_ipa(cell_text):
+                        continue
+                    if any(c in _REAL_IPA_CHARS for c in cell_text):
+                        continue
+                    _words_in_text = re.findall(r'[A-Za-zÄÖÜäöüß]{3,}', cell_text)
+                    if len(_words_in_text) >= 3:
+                        continue
+
+                # Find headword in previous row, same column
+                prev_ri = rows_sorted[idx - 1]["index"]
+                prev_same_col = [
+                    c for c in z_cells
+                    if c.get("row_index") == prev_ri
+                    and c.get("col_type") == ct
+                ]
+                if not prev_same_col:
+                    continue
+                prev_text = prev_same_col[0].get("text", "")
+                fixed = fix_ipa_continuation_cell(
+                    cell_text, prev_text, pronunciation="british",
+                )
+                if fixed != cell_text:
+                    cell["text"] = fixed
+                    ipa_cont_fixed += 1
+                    logger.info(
+                        "IPA continuation R%d %s: '%s' -> '%s'",
+                        ri, ct, cell_text, fixed,
+                    )
+    if ipa_cont_fixed:
+        logger.info("Fixed %d IPA continuation cells", ipa_cont_fixed)
+
+    return en_col_type, ipa_target_cols, all_content_cols
+
+
+def _extract_page_refs_and_footers(
+    zones_data: List[Dict[str, Any]],
+    page_number_info: Optional[Dict],
+) -> None:
+    """Extract page_ref cells and footer rows from content zones.
+
+    Modifies zones_data in place. Updates page_number_info if a page number
+    footer is found.
+    """
+    _REAL_IPA_CHARS_SET = set("ˈˌəɪɛɒʊʌæɑɔʃʒθðŋ")
+    _PAGE_REF_RE = re.compile(r'^[pP,]?\s*\.?\s*\d+$')
+    _NUMBER_WORDS = {
+        "one", "two", "three", "four", "five", "six", "seven",
+        "eight", "nine", "ten", "eleven", "twelve", "thirteen",
+        "fourteen", "fifteen", "sixteen", "seventeen", "eighteen",
+        "nineteen", "twenty", "thirty", "forty", "fifty", "sixty",
+        "seventy", "eighty", "ninety", "hundred", "thousand", "and",
+        "einhundert", "zweihundert", "dreihundert", "vierhundert",
+        "und", "zehn", "zwanzig", "dreißig", "vierzig", "fünfzig",
+    }
+
+    for z in zones_data:
+        if z.get("zone_type") != "content":
+            continue
+        cells = z.get("cells", [])
+        rows = z.get("rows", [])
+        if not rows:
+            continue
+
+        # Extract column_1 cells that look like page references
+        page_refs = []
+        page_ref_cell_ids = set()
+        for cell in cells:
+            if cell.get("col_type") != "column_1":
+                continue
+            text = (cell.get("text") or "").strip()
+            if not text:
+                continue
+            if not _PAGE_REF_RE.match(text):
+                continue
+            page_refs.append({
+                "row_index": cell.get("row_index"),
+                "text": text,
+                "bbox_pct": cell.get("bbox_pct", {}),
+            })
+            page_ref_cell_ids.add(cell.get("cell_id"))
+
+        # Detect footer: last non-header row if it has only 1 cell
+        footer_rows = []
+        non_header_rows = [r for r in rows if not r.get("is_header")]
+        if non_header_rows:
+            last_row = non_header_rows[-1]
+            last_ri = last_row["index"]
+            last_cells = [c for c in z["cells"]
+                          if c.get("row_index") == last_ri]
+            if len(last_cells) == 1:
+                text = (last_cells[0].get("text") or "").strip()
+                has_real_ipa = any(c in _REAL_IPA_CHARS_SET for c in text)
+                has_commas = ',' in text
+                text_words = set(text.lower().split())
+                is_written_number = len(text_words) >= 2 and text_words.issubset(_NUMBER_WORDS)
+                is_page_number = len(text) <= 20 or is_written_number
+                if (text and not has_real_ipa and not has_commas
+                        and is_page_number
+                        and last_cells[0].get("col_type") != "heading"):
+                    footer_rows.append({
+                        "row_index": last_ri,
+                        "text": text,
+                        "bbox_pct": last_cells[0].get("bbox_pct", {}),
+                    })
+
+        # Classify footer rows
+        page_number_footers = []
+        other_footers = []
+        for fr in footer_rows:
+            ft = fr["text"].strip()
+            digits = "".join(c for c in ft if c.isdigit())
+            if digits and re.match(r'^[\d\s.]+$', ft):
+                page_number_footers.append(fr)
+            elif ft.lower().split() and set(ft.lower().split()).issubset(_NUMBER_WORDS):
+                page_number_footers.append(fr)
+            else:
+                other_footers.append(fr)
+
+        # Remove page-number footer rows from grid entirely
+        if page_number_footers:
+            pn_ris = {fr["row_index"] for fr in page_number_footers}
+            z["cells"] = [c for c in z["cells"] if c.get("row_index") not in pn_ris]
+            z["rows"] = [r for r in z["rows"] if r["index"] not in pn_ris]
+            pn_text = page_number_footers[0]["text"].strip()
+            pn_digits = "".join(c for c in pn_text if c.isdigit())
+            if not page_number_info:
+                page_number_info = {
+                    "text": pn_text,
+                    "y_pct": page_number_footers[0].get("bbox_pct", {}).get("y", 95),
+                }
+                if pn_digits:
+                    page_number_info["number"] = int(pn_digits)
+
+        # Mark remaining footer rows
+        if other_footers:
+            footer_ris = {fr["row_index"] for fr in other_footers}
+            for r in z["rows"]:
+                if r["index"] in footer_ris:
+                    r["is_footer"] = True
+            for c in z["cells"]:
+                if c.get("row_index") in footer_ris:
+                    c["col_type"] = "footer"
+
+        if page_refs or footer_rows:
+            logger.info(
+                "Extracted %d page_refs + %d footer rows (%d page numbers removed) from zone %d",
+                len(page_refs), len(footer_rows), len(page_number_footers),
+                z.get("zone_index", 0),
+            )
+
+        if page_refs:
+            z["page_refs"] = page_refs
+        if other_footers:
+            z["footer"] = other_footers
+
+
+def _convert_slash_ipa(
+    zones_data: List[Dict[str, Any]],
+    skip_ipa: bool,
+    en_col_type: Optional[str],
+) -> None:
+    """Convert slash-delimited IPA to bracket notation.
+
+    Dictionary-style pages print IPA between slashes: "tiger /'taiga/"
+    """
+    _SLASH_IPA_RE = re.compile(
+        r'(\b[a-zA-Z]+[²³¹]?)\s*'   # headword (capture group 1)
+        r"(/[^/]{2,}/)"              # /ipa/ (capture group 2), min 2 chars
+    )
+    _STANDALONE_SLASH_IPA_RE = re.compile(r'^/([^/]{2,})/')
+    _SLASH_IPA_REJECT_RE = re.compile(r'[\s(),]')
+    slash_ipa_fixed = 0
+
+    for z in ([] if skip_ipa else zones_data):
+        for cell in z.get("cells", []):
+            if en_col_type and cell.get("col_type") != en_col_type:
+                continue
+            text = cell.get("text", "")
+            if "/" not in text:
+                continue
+
+            def _replace_slash_ipa(m: re.Match) -> str:
+                nonlocal slash_ipa_fixed
+                headword = m.group(1)
+                ocr_ipa = m.group(2)
+                inner_raw = ocr_ipa.strip("/").strip()
+                if _SLASH_IPA_REJECT_RE.search(inner_raw):
+                    return m.group(0)
+                clean_hw = re.sub(r'[²³¹\d]', '', headword).strip()
+                ipa = _lookup_ipa(clean_hw, "british") if clean_hw else None
+                if ipa:
+                    slash_ipa_fixed += 1
+                    return f"{headword} [{ipa}]"
+                inner = inner_raw.lstrip("'").strip()
+                if inner:
+                    slash_ipa_fixed += 1
+                    return f"{headword} [{inner}]"
+                return m.group(0)
+
+            new_text = _SLASH_IPA_RE.sub(_replace_slash_ipa, text)
+
+            _AFTER_BRACKET_SLASH = re.compile(r'(?<=\])\s*(/[^/]{2,}/)')
+
+            def _replace_trailing_slash(m: re.Match) -> str:
+                nonlocal slash_ipa_fixed
+                inner = m.group(1).strip("/").strip().lstrip("'").strip()
+                if _SLASH_IPA_REJECT_RE.search(inner):
+                    return m.group(0)
+                if inner:
+                    slash_ipa_fixed += 1
+                    return f" [{inner}]"
+                return m.group(0)
+            new_text = _AFTER_BRACKET_SLASH.sub(_replace_trailing_slash, new_text)
+
+            if new_text == text:
+                m = _STANDALONE_SLASH_IPA_RE.match(text)
+                if m:
+                    inner = m.group(1).strip()
+                    if not _SLASH_IPA_REJECT_RE.search(inner):
+                        inner = inner.lstrip("'").strip()
+                        if inner:
+                            new_text = "[" + inner + "]" + text[m.end():]
+                            slash_ipa_fixed += 1
+
+            if new_text != text:
+                cell["text"] = new_text
+
+    if slash_ipa_fixed:
+        logger.info("Step 5h: converted %d slash-IPA to bracket notation", slash_ipa_fixed)

klausur-service/backend/grid/build/zones.py

@@ -0,0 +1,464 @@
+"""
+Grid Build Zones — Phase 2: Image processing, graphic detection, box/zone
+detection and zone-aware grid building.
+
+Extracted from grid_build_core.py for maintainability.
+"""
+
+import logging
+from typing import Any, Dict, List, Optional
+
+import cv2
+import numpy as np
+
+from cv_box_detect import detect_boxes, split_page_into_zones
+from cv_graphic_detect import detect_graphic_elements
+from cv_color_detect import recover_colored_text
+from cv_vocab_types import PageZone
+from ocr_pipeline_session_store import get_session_image
+
+from grid.editor.filters import (
+    _filter_border_strip_words,
+    _filter_border_ghosts,
+    _words_in_zone,
+)
+from grid.editor.zones import (
+    _PIPE_RE_VSPLIT,
+    _detect_vertical_dividers,
+    _split_zone_at_vertical_dividers,
+    _merge_content_zones_across_boxes,
+    _build_zone_grid,
+)
+
+logger = logging.getLogger(__name__)
+
+
+async def _build_zones(
+    session_id: str,
+    session: dict,
+    all_words: List[Dict[str, Any]],
+    graphic_rects: List[Dict[str, int]],
+    content_x: int,
+    content_y: int,
+    content_w: int,
+    content_h: int,
+    img_w: int,
+    img_h: int,
+) -> Dict[str, Any]:
+    """Load image, detect graphics/boxes, build zone-aware grids.
+
+    Returns a dict with keys:
+        zones_data, boxes_detected, recovered_count, border_prefiltered,
+        img_bgr, all_words (modified in-place but returned for clarity).
+    """
+    zones_data: List[Dict[str, Any]] = []
+    boxes_detected = 0
+    recovered_count = 0
+    border_prefiltered = False
+    img_bgr = None
+
+    # 3. Load image for box detection
+    img_png = await get_session_image(session_id, "cropped")
+    if not img_png:
+        img_png = await get_session_image(session_id, "dewarped")
+    if not img_png:
+        img_png = await get_session_image(session_id, "original")
+
+    if img_png:
+        # Decode image for color detection + box detection
+        arr = np.frombuffer(img_png, dtype=np.uint8)
+        img_bgr = cv2.imdecode(arr, cv2.IMREAD_COLOR)
+
+        if img_bgr is not None:
+            # --- 3a. Detect graphic/image regions via CV and hard-filter ---
+            sig_words = [w for w in all_words if len((w.get("text") or "").strip()) >= 3]
+            fresh_graphics = detect_graphic_elements(img_bgr, sig_words)
+            if fresh_graphics:
+                fresh_rects = [
+                    {"x": g.x, "y": g.y, "w": g.width, "h": g.height}
+                    for g in fresh_graphics
+                ]
+                graphic_rects.extend(fresh_rects)
+                logger.info(
+                    "build-grid session %s: detected %d graphic region(s) via CV",
+                    session_id, len(fresh_graphics),
+                )
+                # Hard-filter words inside newly detected graphic regions
+                before = len(all_words)
+                all_words[:] = [
+                    w for w in all_words
+                    if not any(
+                        gr["x"] <= w["left"] + w.get("width", 0) / 2 <= gr["x"] + gr["w"]
+                        and gr["y"] <= w["top"] + w.get("height", 0) / 2 <= gr["y"] + gr["h"]
+                        for gr in fresh_rects
+                    )
+                ]
+                removed = before - len(all_words)
+                if removed:
+                    logger.info(
+                        "build-grid session %s: hard-removed %d words inside %d fresh graphic region(s)",
+                        session_id, removed, len(fresh_rects),
+                    )
+
+            # --- Recover colored text that OCR missed (before grid building) ---
+            recovered = recover_colored_text(img_bgr, all_words)
+            if recovered and graphic_rects:
+                # Filter recovered chars inside graphic regions
+                recovered = [
+                    r for r in recovered
+                    if not any(
+                        gr["x"] <= r["left"] + r.get("width", 0) / 2 <= gr["x"] + gr["w"]
+                        and gr["y"] <= r["top"] + r.get("height", 0) / 2 <= gr["y"] + gr["h"]
+                        for gr in graphic_rects
+                    )
+                ]
+            if recovered:
+                recovered_count = len(recovered)
+                all_words.extend(recovered)
+                logger.info(
+                    "build-grid session %s: +%d recovered colored words",
+                    session_id, recovered_count,
+                )
+
+            # Detect bordered boxes
+            boxes = detect_boxes(
+                img_bgr,
+                content_x=content_x,
+                content_w=content_w,
+                content_y=content_y,
+                content_h=content_h,
+            )
+            boxes_detected = len(boxes)
+
+            if boxes:
+                # Filter border ghost words before grid building
+                all_words_new, ghost_count = _filter_border_ghosts(all_words, boxes)
+                if ghost_count:
+                    all_words[:] = all_words_new
+                    logger.info(
+                        "build-grid session %s: removed %d border ghost words",
+                        session_id, ghost_count,
+                    )
+
+                # Split page into zones
+                page_zones = split_page_into_zones(
+                    content_x, content_y, content_w, content_h, boxes
+                )
+
+                # Merge content zones separated by box zones
+                page_zones = _merge_content_zones_across_boxes(
+                    page_zones, content_x, content_w
+                )
+
+                # 3b. Detect vertical dividers and split content zones
+                page_zones, border_prefiltered_vd = _detect_and_split_vertical_dividers(
+                    page_zones, all_words
+                )
+
+                # --- First pass: build grids per zone independently ---
+                zone_grids = _build_grids_per_zone(
+                    page_zones, all_words, img_w, img_h
+                )
+                border_prefiltered = border_prefiltered or any(
+                    zg.get("_border_prefiltered") for zg in zone_grids
+                )
+
+                # --- Second pass: merge column boundaries from all content zones ---
+                _merge_content_zone_columns(
+                    zone_grids, all_words, content_w, img_w, img_h, session_id
+                )
+
+                # --- Build zones_data from zone_grids ---
+                for zg in zone_grids:
+                    pz = zg["pz"]
+                    grid = zg["grid"]
+                    grid.pop("_raw_columns", None)
+
+                    zone_entry: Dict[str, Any] = {
+                        "zone_index": pz.index,
+                        "zone_type": pz.zone_type,
+                        "bbox_px": {
+                            "x": pz.x, "y": pz.y,
+                            "w": pz.width, "h": pz.height,
+                        },
+                        "bbox_pct": {
+                            "x": round(pz.x / img_w * 100, 2) if img_w else 0,
+                            "y": round(pz.y / img_h * 100, 2) if img_h else 0,
+                            "w": round(pz.width / img_w * 100, 2) if img_w else 0,
+                            "h": round(pz.height / img_h * 100, 2) if img_h else 0,
+                        },
+                        "border": None,
+                        "word_count": len(zg["words"]),
+                        **grid,
+                    }
+
+                    if pz.box:
+                        zone_entry["border"] = {
+                            "thickness": pz.box.border_thickness,
+                            "confidence": pz.box.confidence,
+                        }
+
+                    if pz.image_overlays:
+                        zone_entry["image_overlays"] = pz.image_overlays
+
+                    if pz.layout_hint:
+                        zone_entry["layout_hint"] = pz.layout_hint
+                    if pz.vsplit_group is not None:
+                        zone_entry["vsplit_group"] = pz.vsplit_group
+
+                    zones_data.append(zone_entry)
+
+    # 4. Fallback: no boxes detected -> single zone with all words
+    if not zones_data:
+        before = len(all_words)
+        filtered_words = [
+            w for w in all_words
+            if not (w.get("recovered") and len(w.get("text", "").strip()) <= 2)
+        ]
+        removed = before - len(filtered_words)
+        if removed:
+            logger.info(
+                "build-grid session %s: filtered %d recovered artifacts (fallback zone)",
+                session_id, removed,
+            )
+        filtered_words, bs_removed = _filter_border_strip_words(filtered_words)
+        if bs_removed:
+            border_prefiltered = True
+            logger.info(
+                "build-grid session %s: pre-filtered %d border-strip words",
+                session_id, bs_removed,
+            )
+        grid = _build_zone_grid(
+            filtered_words, content_x, content_y, content_w, content_h,
+            0, img_w, img_h,
+        )
+        grid.pop("_raw_columns", None)
+        zones_data.append({
+            "zone_index": 0,
+            "zone_type": "content",
+            "bbox_px": {
+                "x": content_x, "y": content_y,
+                "w": content_w, "h": content_h,
+            },
+            "bbox_pct": {
+                "x": round(content_x / img_w * 100, 2) if img_w else 0,
+                "y": round(content_y / img_h * 100, 2) if img_h else 0,
+                "w": round(content_w / img_w * 100, 2) if img_w else 0,
+                "h": round(content_h / img_h * 100, 2) if img_h else 0,
+            },
+            "border": None,
+            "word_count": len(all_words),
+            **grid,
+        })
+
+    return {
+        "zones_data": zones_data,
+        "boxes_detected": boxes_detected,
+        "recovered_count": recovered_count,
+        "border_prefiltered": border_prefiltered,
+        "img_bgr": img_bgr,
+    }
+
+
+def _detect_and_split_vertical_dividers(
+    page_zones: List[PageZone],
+    all_words: List[Dict[str, Any]],
+) -> tuple:
+    """Detect vertical dividers and split content zones.
+
+    Returns (expanded_zones, border_prefiltered_from_vsplit).
+    """
+    vsplit_group_counter = 0
+    expanded_zones: List = []
+    for pz in page_zones:
+        if pz.zone_type != "content":
+            expanded_zones.append(pz)
+            continue
+        zone_words = _words_in_zone(
+            all_words, pz.y, pz.height, pz.x, pz.width
+        )
+        divider_xs = _detect_vertical_dividers(
+            zone_words, pz.x, pz.width, pz.y, pz.height
+        )
+        if divider_xs:
+            sub_zones = _split_zone_at_vertical_dividers(
+                pz, divider_xs, vsplit_group_counter
+            )
+            expanded_zones.extend(sub_zones)
+            vsplit_group_counter += 1
+            # Remove pipe words so they don't appear in sub-zones
+            pipe_ids = set(
+                id(w) for w in zone_words
+                if _PIPE_RE_VSPLIT.match((w.get("text") or "").strip())
+            )
+            all_words[:] = [w for w in all_words if id(w) not in pipe_ids]
+            logger.info(
+                "build-grid: vertical split zone %d at x=%s -> %d sub-zones",
+                pz.index, [int(x) for x in divider_xs], len(sub_zones),
+            )
+        else:
+            expanded_zones.append(pz)
+    # Re-index zones
+    for i, pz in enumerate(expanded_zones):
+        pz.index = i
+    return expanded_zones, False
+
+
+def _build_grids_per_zone(
+    page_zones: List[PageZone],
+    all_words: List[Dict[str, Any]],
+    img_w: int,
+    img_h: int,
+) -> List[Dict[str, Any]]:
+    """Build grids for each zone independently (first pass)."""
+    zone_grids: List[Dict] = []
+
+    for pz in page_zones:
+        zone_words = _words_in_zone(
+            all_words, pz.y, pz.height, pz.x, pz.width
+        )
+        if pz.zone_type == "content":
+            logger.info(
+                "build-grid zone %d (%s): bounds x=%d..%d y=%d..%d -> %d/%d words",
+                pz.index, pz.zone_type,
+                pz.x, pz.x + pz.width, pz.y, pz.y + pz.height,
+                len(zone_words), len(all_words),
+            )
+        # Filter recovered single-char artifacts in ALL zones
+        before = len(zone_words)
+        zone_words = [
+            w for w in zone_words
+            if not (
+                w.get("recovered")
+                and len(w.get("text", "").strip()) <= 2
+            )
+        ]
+        removed = before - len(zone_words)
+        if removed:
+            logger.info(
+                "build-grid: filtered %d recovered artifacts from %s zone %d",
+                removed, pz.zone_type, pz.index,
+            )
+        # Filter words inside image overlay regions (merged box zones)
+        if pz.image_overlays:
+            before_ov = len(zone_words)
+            zone_words = [
+                w for w in zone_words
+                if not any(
+                    ov["y"] <= w["top"] + w["height"] / 2 <= ov["y"] + ov["height"]
+                    and ov["x"] <= w["left"] + w["width"] / 2 <= ov["x"] + ov["width"]
+                    for ov in pz.image_overlays
+                )
+            ]
+            ov_removed = before_ov - len(zone_words)
+            if ov_removed:
+                logger.info(
+                    "build-grid: filtered %d words inside image overlays from zone %d",
+                    ov_removed, pz.index,
+                )
+        zone_words, bs_removed = _filter_border_strip_words(zone_words)
+        bp = False
+        if bs_removed:
+            bp = True
+            logger.info(
+                "build-grid: pre-filtered %d border-strip words from zone %d",
+                bs_removed, pz.index,
+            )
+        grid = _build_zone_grid(
+            zone_words, pz.x, pz.y, pz.width, pz.height,
+            pz.index, img_w, img_h,
+            skip_first_row_header=bool(pz.image_overlays),
+        )
+        zone_grids.append({
+            "pz": pz, "words": zone_words, "grid": grid,
+            "_border_prefiltered": bp,
+        })
+
+    return zone_grids
+
+
+def _merge_content_zone_columns(
+    zone_grids: List[Dict[str, Any]],
+    all_words: List[Dict[str, Any]],
+    content_w: int,
+    img_w: int,
+    img_h: int,
+    session_id: str,
+) -> None:
+    """Second pass: merge column boundaries from all content zones.
+
+    Modifies zone_grids in place.
+    """
+    content_zones = [
+        zg for zg in zone_grids
+        if zg["pz"].zone_type == "content"
+        and zg["pz"].vsplit_group is None
+    ]
+    if len(content_zones) <= 1:
+        return
+
+    # Collect column split points (x_min of non-first columns)
+    all_split_xs: List[float] = []
+    for zg in content_zones:
+        raw_cols = zg["grid"].get("_raw_columns", [])
+        for col in raw_cols[1:]:
+            all_split_xs.append(col["x_min"])
+
+    if not all_split_xs:
+        return
+
+    all_split_xs.sort()
+    merge_distance = max(25, int(content_w * 0.03))
+    merged_xs = [all_split_xs[0]]
+    for x in all_split_xs[1:]:
+        if x - merged_xs[-1] < merge_distance:
+            merged_xs[-1] = (merged_xs[-1] + x) / 2
+        else:
+            merged_xs.append(x)
+
+    total_cols = len(merged_xs) + 1
+    max_zone_cols = max(
+        len(zg["grid"].get("_raw_columns", []))
+        for zg in content_zones
+    )
+
+    if total_cols < max_zone_cols:
+        return
+
+    cx_min = min(w["left"] for w in all_words)
+    cx_max = max(w["left"] + w["width"] for w in all_words)
+    merged_columns: List[Dict[str, Any]] = []
+    prev_x = cx_min
+    for i, sx in enumerate(merged_xs):
+        merged_columns.append({
+            "index": i,
+            "type": f"column_{i + 1}",
+            "x_min": prev_x,
+            "x_max": sx,
+        })
+        prev_x = sx
+    merged_columns.append({
+        "index": len(merged_xs),
+        "type": f"column_{len(merged_xs) + 1}",
+        "x_min": prev_x,
+        "x_max": cx_max,
+    })
+
+    # Re-build ALL content zones with merged columns
+    for zg in zone_grids:
+        pz = zg["pz"]
+        if pz.zone_type == "content":
+            grid = _build_zone_grid(
+                zg["words"], pz.x, pz.y,
+                pz.width, pz.height,
+                pz.index, img_w, img_h,
+                global_columns=merged_columns,
+                skip_first_row_header=bool(pz.image_overlays),
+            )
+            zg["grid"] = grid
+    logger.info(
+        "build-grid session %s: union of %d content "
+        "zones -> %d merged columns (max single zone: %d)",
+        session_id, len(content_zones),
+        total_cols, max_zone_cols,
+    )

klausur-service/backend/grid/editor/__init__.py

@@ -0,0 +1,15 @@
+"""
+Grid Editor sub-package — FastAPI endpoints and helper functions.
+
+Modules:
+  - api          — barrel re-export (combined router + _build_grid_core)
+  - api_grid     — build-grid, save-grid, get-grid endpoints
+  - api_gutter   — gutter-repair endpoints
+  - api_box      — build-box-grids endpoints
+  - api_unified  — build-unified-grid endpoints
+  - helpers      — barrel re-export of all helper symbols
+  - columns      — column detection, cross-column splitting
+  - filters      — word/zone filtering, border ghosts
+  - headers      — header/heading detection, colspan detection
+  - zones        — vertical dividers, zone splitting/merging
+"""

klausur-service/backend/grid/editor/api.py

@@ -0,0 +1,31 @@
+"""
+Grid Editor API — barrel re-export.
+
+The actual endpoints live in:
+  - grid_editor_api_grid.py   (build-grid, rerun-ocr, save-grid, get-grid)
+  - grid_editor_api_gutter.py (gutter-repair, gutter-repair/apply)
+  - grid_editor_api_box.py    (build-box-grids)
+  - grid_editor_api_unified.py (build-unified-grid, unified-grid)
+
+This module re-exports the combined router and key symbols so that
+existing `from grid_editor_api import router` / `from grid_editor_api import _build_grid_core`
+continue to work unchanged.
+"""
+
+from fastapi import APIRouter
+
+from .api_grid import router as _grid_router
+from .api_gutter import router as _gutter_router
+from .api_box import router as _box_router
+from .api_unified import router as _unified_router
+
+# Re-export _build_grid_core so callers that do
+# `from grid_editor_api import _build_grid_core` keep working.
+from grid.build.core import _build_grid_core  # noqa: F401
+
+# Merge all sub-routers into one combined router
+router = APIRouter()
+router.include_router(_grid_router)
+router.include_router(_gutter_router)
+router.include_router(_box_router)
+router.include_router(_unified_router)

klausur-service/backend/grid/editor/api_box.py

@@ -0,0 +1,177 @@
+"""
+Grid Editor API — box-grid-review endpoints.
+"""
+
+import logging
+
+from fastapi import APIRouter, HTTPException, Request
+
+from .filters import _words_in_zone
+from ocr_pipeline_session_store import (
+    get_session_db,
+    update_session_db,
+)
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/api/v1/ocr-pipeline", tags=["grid-editor"])
+
+
+@router.post("/sessions/{session_id}/build-box-grids")
+async def build_box_grids(session_id: str, request: Request):
+    """Rebuild grid structure for all detected boxes with layout-aware detection.
+
+    Uses structure_result.boxes (from Step 7) as the source of box coordinates,
+    and raw_paddle_words as OCR word source. Creates or updates box zones in
+    the grid_editor_result.
+
+    Optional body: { "overrides": { "0": "bullet_list" } }
+    Maps box_index -> forced layout_type.
+    """
+    session = await get_session_db(session_id)
+    if not session:
+        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
+
+    grid_data = session.get("grid_editor_result")
+    if not grid_data:
+        raise HTTPException(status_code=400, detail="No grid data. Run build-grid first.")
+
+    # Get raw OCR words (with top/left/width/height keys)
+    word_result = session.get("word_result") or {}
+    all_words = word_result.get("raw_paddle_words") or word_result.get("raw_tesseract_words") or []
+    if not all_words:
+        raise HTTPException(status_code=400, detail="No raw OCR words available.")
+
+    # Get detected boxes from structure_result
+    structure_result = session.get("structure_result") or {}
+    gt = session.get("ground_truth") or {}
+    if not structure_result:
+        structure_result = gt.get("structure_result") or {}
+    detected_boxes = structure_result.get("boxes") or []
+    if not detected_boxes:
+        return {"session_id": session_id, "box_zones_rebuilt": 0, "spell_fixes": 0, "message": "No boxes detected"}
+
+    # Filter out false-positive boxes in header/footer margins.
+    img_h_for_filter = grid_data.get("image_height", 0) or word_result.get("image_height", 0)
+    if img_h_for_filter > 0:
+        margin_frac = 0.07  # 7% of image height
+        margin_top = img_h_for_filter * margin_frac
+        margin_bottom = img_h_for_filter * (1 - margin_frac)
+        filtered = []
+        for box in detected_boxes:
+            by = box.get("y", 0)
+            bh = box.get("h", 0)
+            box_center_y = by + bh / 2
+            if box_center_y < margin_top or box_center_y > margin_bottom:
+                logger.info("build-box-grids: skipping header/footer box at y=%d h=%d (center=%.0f, margins=%.0f/%.0f)",
+                            by, bh, box_center_y, margin_top, margin_bottom)
+                continue
+            filtered.append(box)
+        detected_boxes = filtered
+
+    body = {}
+    try:
+        body = await request.json()
+    except Exception:
+        pass
+    layout_overrides = body.get("overrides", {})
+
+    from cv_box_layout import build_box_zone_grid
+
+    img_w = grid_data.get("image_width", 0) or word_result.get("image_width", 0)
+    img_h = grid_data.get("image_height", 0) or word_result.get("image_height", 0)
+
+    zones = grid_data.get("zones", [])
+
+    # Find highest existing zone_index
+    max_zone_idx = max((z.get("zone_index", 0) for z in zones), default=-1)
+
+    # Remove old box zones (we'll rebuild them)
+    zones = [z for z in zones if z.get("zone_type") != "box"]
+
+    box_count = 0
+    spell_fixes = 0
+
+    for box_idx, box in enumerate(detected_boxes):
+        bx = box.get("x", 0)
+        by = box.get("y", 0)
+        bw = box.get("w", 0)
+        bh = box.get("h", 0)
+
+        if bw <= 0 or bh <= 0:
+            continue
+
+        # Filter raw OCR words inside this box
+        zone_words = _words_in_zone(all_words, by, bh, bx, bw)
+        if not zone_words:
+            logger.info("Box %d: no words found in bbox (%d,%d,%d,%d)", box_idx, bx, by, bw, bh)
+            continue
+
+        zone_idx = max_zone_idx + 1 + box_idx
+        forced_layout = layout_overrides.get(str(box_idx))
+
+        # Build box grid
+        box_grid = build_box_zone_grid(
+            zone_words, bx, by, bw, bh,
+            zone_idx, img_w, img_h,
+            layout_type=forced_layout,
+        )
+
+        # Apply SmartSpellChecker to all box cells
+        try:
+            from smart_spell import SmartSpellChecker
+            ssc = SmartSpellChecker()
+            for cell in box_grid.get("cells", []):
+                text = cell.get("text", "")
+                if not text:
+                    continue
+                result = ssc.correct_text(text, lang="auto")
+                if result.changed:
+                    cell["text"] = result.corrected
+                    spell_fixes += 1
+        except ImportError:
+            pass
+
+        # Build zone entry
+        zone_entry = {
+            "zone_index": zone_idx,
+            "zone_type": "box",
+            "bbox_px": {"x": bx, "y": by, "w": bw, "h": bh},
+            "bbox_pct": {
+                "x": round(bx / img_w * 100, 2) if img_w else 0,
+                "y": round(by / img_h * 100, 2) if img_h else 0,
+                "w": round(bw / img_w * 100, 2) if img_w else 0,
+                "h": round(bh / img_h * 100, 2) if img_h else 0,
+            },
+            "border": None,
+            "word_count": len(zone_words),
+            "columns": box_grid["columns"],
+            "rows": box_grid["rows"],
+            "cells": box_grid["cells"],
+            "header_rows": box_grid.get("header_rows", []),
+            "box_layout_type": box_grid.get("box_layout_type", "flowing"),
+            "box_grid_reviewed": False,
+            "box_bg_color": box.get("bg_color_name", ""),
+            "box_bg_hex": box.get("bg_color_hex", ""),
+        }
+        zones.append(zone_entry)
+        box_count += 1
+
+    # Sort zones by y-position for correct reading order
+    zones.sort(key=lambda z: z.get("bbox_px", {}).get("y", 0))
+
+    grid_data["zones"] = zones
+    await update_session_db(session_id, grid_editor_result=grid_data)
+
+    logger.info(
+        "build-box-grids session %s: %d boxes processed (%d words spell-fixed) from %d detected",
+        session_id, box_count, spell_fixes, len(detected_boxes),
+    )
+
+    return {
+        "session_id": session_id,
+        "box_zones_rebuilt": box_count,
+        "total_detected_boxes": len(detected_boxes),
+        "spell_fixes": spell_fixes,
+        "zones": zones,
+    }

klausur-service/backend/grid/editor/api_grid.py

@@ -0,0 +1,334 @@
+"""
+Grid Editor API — grid build, save, and retrieve endpoints.
+"""
+
+import logging
+
+from fastapi import APIRouter, HTTPException, Query, Request
+
+from grid.build.core import _build_grid_core
+from ocr_pipeline_session_store import (
+    get_session_db,
+    update_session_db,
+)
+from ocr_pipeline_common import (
+    _cache,
+    _load_session_to_cache,
+    _get_cached,
+)
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/api/v1/ocr-pipeline", tags=["grid-editor"])
+
+
+@router.post("/sessions/{session_id}/build-grid")
+async def build_grid(
+    session_id: str,
+    ipa_mode: str = Query("auto", pattern="^(auto|all|de|en|none)$"),
+    syllable_mode: str = Query("auto", pattern="^(auto|all|de|en|none)$"),
+    enhance: bool = Query(True, description="Step 3: CLAHE + denoise for degraded scans"),
+    max_cols: int = Query(0, description="Step 2: Max column count (0=unlimited)"),
+    min_conf: int = Query(0, description="Step 1: Min OCR confidence (0=auto)"),
+):
+    """Build a structured, zone-aware grid from existing Kombi word results.
+
+    Requires that paddle-kombi or rapid-kombi has already been run on the session.
+    Uses the image for box detection and the word positions for grid structuring.
+
+    Query params:
+        ipa_mode: "auto" (only when English IPA detected), "all" (force), "none" (skip)
+        syllable_mode: "auto" (only when original has dividers), "all" (force), "none" (skip)
+
+    Returns a StructuredGrid with zones, each containing their own
+    columns, rows, and cells — ready for the frontend Excel-like editor.
+    """
+    session = await get_session_db(session_id)
+    if not session:
+        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
+
+    try:
+        result = await _build_grid_core(
+            session_id, session,
+            ipa_mode=ipa_mode, syllable_mode=syllable_mode,
+            enhance=enhance,
+            max_columns=max_cols if max_cols > 0 else None,
+            min_conf=min_conf if min_conf > 0 else None,
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+    # Save automatic grid snapshot for later comparison with manual corrections
+    # Lazy import to avoid circular dependency with ocr_pipeline_regression
+    from ocr_pipeline_regression import _build_reference_snapshot
+
+    wr = session.get("word_result") or {}
+    engine = wr.get("ocr_engine", "")
+    if engine in ("kombi", "rapid_kombi"):
+        auto_pipeline = "kombi"
+    elif engine == "paddle_direct":
+        auto_pipeline = "paddle-direct"
+    else:
+        auto_pipeline = "pipeline"
+    auto_snapshot = _build_reference_snapshot(result, pipeline=auto_pipeline)
+
+    gt = session.get("ground_truth") or {}
+    gt["auto_grid_snapshot"] = auto_snapshot
+
+    # Persist to DB and advance current_step to 11 (reconstruction complete)
+    await update_session_db(session_id, grid_editor_result=result, ground_truth=gt, current_step=11)
+
+    logger.info(
+        "build-grid session %s: %d zones, %d cols, %d rows, %d cells, "
+        "%d boxes in %.2fs",
+        session_id,
+        len(result.get("zones", [])),
+        result.get("summary", {}).get("total_columns", 0),
+        result.get("summary", {}).get("total_rows", 0),
+        result.get("summary", {}).get("total_cells", 0),
+        result.get("boxes_detected", 0),
+        result.get("duration_seconds", 0),
+    )
+
+    return result
+
+
+@router.post("/sessions/{session_id}/rerun-ocr-and-build-grid")
+async def rerun_ocr_and_build_grid(
+    session_id: str,
+    ipa_mode: str = Query("auto", pattern="^(auto|all|de|en|none)$"),
+    syllable_mode: str = Query("auto", pattern="^(auto|all|de|en|none)$"),
+    enhance: bool = Query(True, description="Step 3: CLAHE + denoise for degraded scans"),
+    max_cols: int = Query(0, description="Step 2: Max column count (0=unlimited)"),
+    min_conf: int = Query(0, description="Step 1: Min OCR confidence (0=auto)"),
+    vision_fusion: bool = Query(False, description="Step 4: Vision-LLM fusion for degraded scans"),
+    doc_category: str = Query("", description="Document type for Vision-LLM prompt context"),
+):
+    """Re-run OCR with quality settings, then rebuild the grid.
+
+    Unlike build-grid (which only rebuilds from existing words),
+    this endpoint re-runs the full OCR pipeline on the cropped image
+    with optional CLAHE enhancement, then builds the grid.
+
+    Steps executed: Image Enhancement -> OCR -> Grid Build
+    """
+    session = await get_session_db(session_id)
+    if not session:
+        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
+
+    import time as _time
+    t0 = _time.time()
+
+    # 1. Load the cropped/dewarped image from cache or session
+    if session_id not in _cache:
+        await _load_session_to_cache(session_id)
+    cached = _get_cached(session_id)
+
+    dewarped_bgr = cached.get("cropped_bgr") if cached.get("cropped_bgr") is not None else cached.get("dewarped_bgr")
+    if dewarped_bgr is None:
+        raise HTTPException(status_code=400, detail="No cropped/dewarped image available. Run preprocessing steps first.")
+
+    img_h, img_w = dewarped_bgr.shape[:2]
+    ocr_input = dewarped_bgr.copy()
+
+    # 2. Scan quality assessment
+    scan_quality_info = {}
+    try:
+        from scan_quality import score_scan_quality
+        quality_report = score_scan_quality(ocr_input)
+        scan_quality_info = quality_report.to_dict()
+        actual_min_conf = min_conf if min_conf > 0 else quality_report.recommended_min_conf
+    except Exception as e:
+        logger.warning(f"rerun-ocr: scan quality failed: {e}")
+        actual_min_conf = min_conf if min_conf > 0 else 40
+
+    # 3. Image enhancement (Step 3)
+    is_degraded = scan_quality_info.get("is_degraded", False)
+    if enhance and is_degraded:
+        try:
+            from ocr_image_enhance import enhance_for_ocr
+            ocr_input = enhance_for_ocr(ocr_input, is_degraded=True)
+            logger.info("rerun-ocr: CLAHE enhancement applied")
+        except Exception as e:
+            logger.warning(f"rerun-ocr: enhancement failed: {e}")
+
+    # 4. Run dual-engine OCR
+    from PIL import Image
+    import pytesseract
+
+    # RapidOCR
+    rapid_words = []
+    try:
+        from cv_ocr_engines import ocr_region_rapid
+        from cv_vocab_types import PageRegion
+        full_region = PageRegion(type="full_page", x=0, y=0, width=img_w, height=img_h)
+        rapid_words = ocr_region_rapid(ocr_input, full_region) or []
+    except Exception as e:
+        logger.warning(f"rerun-ocr: RapidOCR failed: {e}")
+
+    # Tesseract
+    pil_img = Image.fromarray(ocr_input[:, :, ::-1])
+    data = pytesseract.image_to_data(pil_img, lang='eng+deu', config='--psm 6 --oem 3', output_type=pytesseract.Output.DICT)
+    tess_words = []
+    for i in range(len(data["text"])):
+        text = (data["text"][i] or "").strip()
+        conf_raw = str(data["conf"][i])
+        conf = int(conf_raw) if conf_raw.lstrip("-").isdigit() else -1
+        if not text or conf < actual_min_conf:
+            continue
+        tess_words.append({
+            "text": text, "left": data["left"][i], "top": data["top"][i],
+            "width": data["width"][i], "height": data["height"][i], "conf": conf,
+        })
+
+    # 5. Merge OCR results
+    from ocr_pipeline_ocr_merge import _split_paddle_multi_words, _merge_paddle_tesseract, _deduplicate_words
+    rapid_split = _split_paddle_multi_words(rapid_words) if rapid_words else []
+    if rapid_split or tess_words:
+        merged_words = _merge_paddle_tesseract(rapid_split, tess_words)
+        merged_words = _deduplicate_words(merged_words)
+    else:
+        merged_words = tess_words
+
+    # 6. Store updated word_result in session
+    cells_for_storage = [{"text": w["text"], "left": w["left"], "top": w["top"],
+                          "width": w["width"], "height": w["height"], "conf": w.get("conf", 0)}
+                         for w in merged_words]
+    word_result = {
+        "cells": [{"text": " ".join(w["text"] for w in merged_words),
+                    "word_boxes": cells_for_storage}],
+        "image_width": img_w,
+        "image_height": img_h,
+        "ocr_engine": "rapid_kombi",
+        "word_count": len(merged_words),
+        "raw_paddle_words": rapid_words,
+    }
+    # 6b. Vision-LLM Fusion (Step 4) — correct OCR using Vision model
+    vision_applied = False
+    if vision_fusion:
+        try:
+            from vision_ocr_fusion import vision_fuse_ocr
+            category = doc_category or session.get("document_category") or "vokabelseite"
+            logger.info(f"rerun-ocr: running Vision-LLM fusion (category={category})")
+            merged_words = await vision_fuse_ocr(ocr_input, merged_words, category)
+            vision_applied = True
+            # Rebuild storage from fused words
+            cells_for_storage = [{"text": w["text"], "left": w["left"], "top": w["top"],
+                                  "width": w["width"], "height": w["height"], "conf": w.get("conf", 0)}
+                                 for w in merged_words]
+            word_result["cells"] = [{"text": " ".join(w["text"] for w in merged_words),
+                                     "word_boxes": cells_for_storage}]
+            word_result["word_count"] = len(merged_words)
+            word_result["ocr_engine"] = "vision_fusion"
+        except Exception as e:
+            logger.warning(f"rerun-ocr: Vision-LLM fusion failed: {e}")
+
+    await update_session_db(session_id, word_result=word_result)
+
+    # Reload session with updated word_result
+    session = await get_session_db(session_id)
+
+    ocr_duration = _time.time() - t0
+    logger.info(
+        "rerun-ocr session %s: %d words (rapid=%d, tess=%d, merged=%d) in %.1fs "
+        "(enhance=%s, min_conf=%d, quality=%s)",
+        session_id, len(merged_words), len(rapid_words), len(tess_words),
+        len(merged_words), ocr_duration, enhance, actual_min_conf,
+        scan_quality_info.get("quality_pct", "?"),
+    )
+
+    # 7. Build grid from new words
+    try:
+        result = await _build_grid_core(
+            session_id, session,
+            ipa_mode=ipa_mode, syllable_mode=syllable_mode,
+            enhance=enhance,
+            max_columns=max_cols if max_cols > 0 else None,
+            min_conf=min_conf if min_conf > 0 else None,
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+    # Persist grid
+    await update_session_db(session_id, grid_editor_result=result, current_step=11)
+
+    # Add quality info to response
+    result["scan_quality"] = scan_quality_info
+    result["ocr_stats"] = {
+        "rapid_words": len(rapid_words),
+        "tess_words": len(tess_words),
+        "merged_words": len(merged_words),
+        "min_conf_used": actual_min_conf,
+        "enhance_applied": enhance and is_degraded,
+        "vision_fusion_applied": vision_applied,
+        "document_category": doc_category or session.get("document_category", ""),
+        "ocr_duration_seconds": round(ocr_duration, 1),
+    }
+
+    total_duration = _time.time() - t0
+    logger.info(
+        "rerun-ocr+build-grid session %s: %d zones, %d cols, %d cells in %.1fs",
+        session_id,
+        len(result.get("zones", [])),
+        result.get("summary", {}).get("total_columns", 0),
+        result.get("summary", {}).get("total_cells", 0),
+        total_duration,
+    )
+
+    return result
+
+
+@router.post("/sessions/{session_id}/save-grid")
+async def save_grid(session_id: str, request: Request):
+    """Save edited grid data from the frontend Excel-like editor.
+
+    Receives the full StructuredGrid with user edits (text changes,
+    formatting changes like bold columns, header rows, etc.) and
+    persists it to the session's grid_editor_result.
+    """
+    session = await get_session_db(session_id)
+    if not session:
+        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
+
+    body = await request.json()
+
+    # Validate basic structure
+    if "zones" not in body:
+        raise HTTPException(status_code=400, detail="Missing 'zones' in request body")
+
+    # Preserve metadata from the original build
+    existing = session.get("grid_editor_result") or {}
+    result = {
+        "session_id": session_id,
+        "image_width": body.get("image_width", existing.get("image_width", 0)),
+        "image_height": body.get("image_height", existing.get("image_height", 0)),
+        "zones": body["zones"],
+        "boxes_detected": body.get("boxes_detected", existing.get("boxes_detected", 0)),
+        "summary": body.get("summary", existing.get("summary", {})),
+        "formatting": body.get("formatting", existing.get("formatting", {})),
+        "duration_seconds": existing.get("duration_seconds", 0),
+        "edited": True,
+    }
+
+    await update_session_db(session_id, grid_editor_result=result, current_step=11)
+
+    logger.info("save-grid session %s: %d zones saved", session_id, len(body["zones"]))
+
+    return {"session_id": session_id, "saved": True}
+
+
+@router.get("/sessions/{session_id}/grid-editor")
+async def get_grid(session_id: str):
+    """Retrieve the current grid editor state for a session."""
+    session = await get_session_db(session_id)
+    if not session:
+        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
+
+    result = session.get("grid_editor_result")
+    if not result:
+        raise HTTPException(
+            status_code=404,
+            detail="No grid editor data. Run build-grid first.",
+        )
+
+    return result

klausur-service/backend/grid/editor/api_gutter.py

@@ -0,0 +1,110 @@
+"""
+Grid Editor API — gutter repair endpoints.
+"""
+
+import logging
+
+from fastapi import APIRouter, HTTPException, Request
+
+from ocr_pipeline_session_store import (
+    get_session_db,
+    update_session_db,
+)
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/api/v1/ocr-pipeline", tags=["grid-editor"])
+
+
+@router.post("/sessions/{session_id}/gutter-repair")
+async def gutter_repair(session_id: str):
+    """Analyse grid for gutter-edge OCR errors and return repair suggestions.
+
+    Detects:
+      - Words truncated/blurred at the book binding (spell_fix)
+      - Words split across rows with missing hyphen chars (hyphen_join)
+    """
+    session = await get_session_db(session_id)
+    if not session:
+        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
+
+    grid_data = session.get("grid_editor_result")
+    if not grid_data:
+        raise HTTPException(
+            status_code=400,
+            detail="No grid data. Run build-grid first.",
+        )
+
+    from cv_gutter_repair import analyse_grid_for_gutter_repair
+
+    image_width = grid_data.get("image_width", 0)
+    result = analyse_grid_for_gutter_repair(grid_data, image_width=image_width)
+
+    # Persist suggestions in ground_truth.gutter_repair (avoids DB migration)
+    gt = session.get("ground_truth") or {}
+    gt["gutter_repair"] = result
+    await update_session_db(session_id, ground_truth=gt)
+
+    logger.info(
+        "gutter-repair session %s: %d suggestions in %.2fs",
+        session_id,
+        result.get("stats", {}).get("suggestions_found", 0),
+        result.get("duration_seconds", 0),
+    )
+
+    return result
+
+
+@router.post("/sessions/{session_id}/gutter-repair/apply")
+async def gutter_repair_apply(session_id: str, request: Request):
+    """Apply accepted gutter repair suggestions to the grid.
+
+    Body: { "accepted": ["suggestion_id_1", "suggestion_id_2", ...] }
+    """
+    session = await get_session_db(session_id)
+    if not session:
+        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
+
+    grid_data = session.get("grid_editor_result")
+    if not grid_data:
+        raise HTTPException(status_code=400, detail="No grid data.")
+
+    gt = session.get("ground_truth") or {}
+    gutter_result = gt.get("gutter_repair")
+    if not gutter_result:
+        raise HTTPException(
+            status_code=400,
+            detail="No gutter repair data. Run gutter-repair first.",
+        )
+
+    body = await request.json()
+    accepted_ids = body.get("accepted", [])
+    if not accepted_ids:
+        return {"applied_count": 0, "changes": []}
+
+    # text_overrides: { suggestion_id: "alternative_text" }
+    # Allows the user to pick a different correction from the alternatives list
+    text_overrides = body.get("text_overrides", {})
+
+    from cv_gutter_repair import apply_gutter_suggestions
+
+    suggestions = gutter_result.get("suggestions", [])
+
+    # Apply user-selected alternatives before passing to apply
+    for s in suggestions:
+        sid = s.get("id", "")
+        if sid in text_overrides and text_overrides[sid]:
+            s["suggested_text"] = text_overrides[sid]
+
+    result = apply_gutter_suggestions(grid_data, accepted_ids, suggestions)
+
+    # Save updated grid back to session
+    await update_session_db(session_id, grid_editor_result=grid_data)
+
+    logger.info(
+        "gutter-repair/apply session %s: %d changes applied",
+        session_id,
+        result.get("applied_count", 0),
+    )
+
+    return result

klausur-service/backend/grid/editor/api_unified.py

@@ -0,0 +1,71 @@
+"""
+Grid Editor API — unified grid endpoints.
+"""
+
+import logging
+
+from fastapi import APIRouter, HTTPException
+
+from ocr_pipeline_session_store import (
+    get_session_db,
+    update_session_db,
+)
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/api/v1/ocr-pipeline", tags=["grid-editor"])
+
+
+@router.post("/sessions/{session_id}/build-unified-grid")
+async def build_unified_grid_endpoint(session_id: str):
+    """Build a single-zone unified grid merging content + box zones.
+
+    Takes the existing multi-zone grid_editor_result and produces a
+    unified grid where boxes are integrated into the main row sequence.
+    Persists as unified_grid_result (preserves original multi-zone data).
+    """
+    session = await get_session_db(session_id)
+    if not session:
+        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
+
+    grid_data = session.get("grid_editor_result")
+    if not grid_data:
+        raise HTTPException(status_code=400, detail="No grid data. Run build-grid first.")
+
+    from unified_grid import build_unified_grid
+
+    result = build_unified_grid(
+        zones=grid_data.get("zones", []),
+        image_width=grid_data.get("image_width", 0),
+        image_height=grid_data.get("image_height", 0),
+        layout_metrics=grid_data.get("layout_metrics", {}),
+    )
+
+    # Persist as separate field (don't overwrite original multi-zone grid)
+    await update_session_db(session_id, unified_grid_result=result)
+
+    logger.info(
+        "build-unified-grid session %s: %d rows, %d cells",
+        session_id,
+        result.get("summary", {}).get("total_rows", 0),
+        result.get("summary", {}).get("total_cells", 0),
+    )
+
+    return result
+
+
+@router.get("/sessions/{session_id}/unified-grid")
+async def get_unified_grid(session_id: str):
+    """Retrieve the unified grid for a session."""
+    session = await get_session_db(session_id)
+    if not session:
+        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
+
+    result = session.get("unified_grid_result")
+    if not result:
+        raise HTTPException(
+            status_code=404,
+            detail="No unified grid. Run build-unified-grid first.",
+        )
+
+    return result

klausur-service/backend/grid/editor/columns.py

@@ -0,0 +1,492 @@
+"""
+Grid Editor — column detection, cross-column splitting, marker merging.
+
+Split from grid_editor_helpers.py for maintainability.
+All functions are pure computation — no HTTP, DB, or session side effects.
+
+Lizenz: Apache 2.0 (kommerziell nutzbar)
+DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+import re
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Cross-column word splitting
+# ---------------------------------------------------------------------------
+
+_spell_cache: Optional[Any] = None
+_spell_loaded = False
+
+
+def _is_recognized_word(text: str) -> bool:
+    """Check if *text* is a recognized German or English word.
+
+    Uses the spellchecker library (same as cv_syllable_detect.py).
+    Returns True for real words like "oder", "Kabel", "Zeitung".
+    Returns False for OCR merge artifacts like "sichzie", "dasZimmer".
+    """
+    global _spell_cache, _spell_loaded
+    if not text or len(text) < 2:
+        return False
+
+    if not _spell_loaded:
+        _spell_loaded = True
+        try:
+            from spellchecker import SpellChecker
+            _spell_cache = SpellChecker(language="de")
+        except Exception:
+            pass
+
+    if _spell_cache is None:
+        return False
+
+    return text.lower() in _spell_cache
+
+
+def _split_cross_column_words(
+    words: List[Dict],
+    columns: List[Dict],
+) -> List[Dict]:
+    """Split word boxes that span across column boundaries.
+
+    When OCR merges adjacent words from different columns (e.g. "sichzie"
+    spanning Col 1 and Col 2, or "dasZimmer" crossing the boundary),
+    split the word box at the column boundary so each piece is assigned
+    to the correct column.
+
+    Only splits when:
+    - The word has significant overlap (>15% of its width) on both sides
+    - AND the word is not a recognized real word (OCR merge artifact), OR
+      the word contains a case transition (lowercase->uppercase) near the
+      boundary indicating two merged words like "dasZimmer".
+    """
+    if len(columns) < 2:
+        return words
+
+    # Column boundaries = midpoints between adjacent column edges
+    boundaries = []
+    for i in range(len(columns) - 1):
+        boundary = (columns[i]["x_max"] + columns[i + 1]["x_min"]) / 2
+        boundaries.append(boundary)
+
+    new_words: List[Dict] = []
+    split_count = 0
+    for w in words:
+        w_left = w["left"]
+        w_width = w["width"]
+        w_right = w_left + w_width
+        text = (w.get("text") or "").strip()
+
+        if not text or len(text) < 4 or w_width < 10:
+            new_words.append(w)
+            continue
+
+        # Find the first boundary this word straddles significantly
+        split_boundary = None
+        for b in boundaries:
+            if w_left < b < w_right:
+                left_part = b - w_left
+                right_part = w_right - b
+                # Both sides must have at least 15% of the word width
+                if left_part > w_width * 0.15 and right_part > w_width * 0.15:
+                    split_boundary = b
+                    break
+
+        if split_boundary is None:
+            new_words.append(w)
+            continue
+
+        # Compute approximate split position in the text.
+        left_width = split_boundary - w_left
+        split_ratio = left_width / w_width
+        approx_pos = len(text) * split_ratio
+
+        # Strategy 1: look for a case transition (lowercase->uppercase) near
+        # the approximate split point — e.g. "dasZimmer" splits at 'Z'.
+        split_char = None
+        search_lo = max(1, int(approx_pos) - 3)
+        search_hi = min(len(text), int(approx_pos) + 2)
+        for i in range(search_lo, search_hi):
+            if text[i - 1].islower() and text[i].isupper():
+                split_char = i
+                break
+
+        # Strategy 2: if no case transition, only split if the whole word
+        # is NOT a real word (i.e. it's an OCR merge artifact like "sichzie").
+        # Real words like "oder", "Kabel", "Zeitung" must not be split.
+        if split_char is None:
+            clean = re.sub(r"[,;:.!?]+$", "", text)  # strip trailing punct
+            if _is_recognized_word(clean):
+                new_words.append(w)
+                continue
+            # Not a real word — use floor of proportional position
+            split_char = max(1, min(len(text) - 1, int(approx_pos)))
+
+        left_text = text[:split_char].rstrip()
+        right_text = text[split_char:].lstrip()
+
+        if len(left_text) < 2 or len(right_text) < 2:
+            new_words.append(w)
+            continue
+
+        right_width = w_width - round(left_width)
+        new_words.append({
+            **w,
+            "text": left_text,
+            "width": round(left_width),
+        })
+        new_words.append({
+            **w,
+            "text": right_text,
+            "left": round(split_boundary),
+            "width": right_width,
+        })
+        split_count += 1
+        logger.info(
+            "split cross-column word %r -> %r + %r at boundary %.0f",
+            text, left_text, right_text, split_boundary,
+        )
+
+    if split_count:
+        logger.info("split %d cross-column word(s)", split_count)
+    return new_words
+
+
+def _cluster_columns_by_alignment(
+    words: List[Dict],
+    zone_w: int,
+    rows: List[Dict],
+) -> List[Dict[str, Any]]:
+    """Detect columns by clustering left-edge alignment across rows.
+
+    Hybrid approach:
+      1. Group words by row, find "group start" positions within each row
+         (words preceded by a large gap or first word in row)
+      2. Cluster group-start left-edges by X-proximity across rows
+      3. Filter by row coverage (how many rows have a group start here)
+      4. Merge nearby clusters
+      5. Build column boundaries
+
+    This filters out mid-phrase word positions (e.g. IPA transcriptions,
+    second words in multi-word entries) by only considering positions
+    where a new word group begins within a row.
+    """
+    if not words or not rows:
+        return []
+
+    total_rows = len(rows)
+    if total_rows == 0:
+        return []
+
+    # --- Group words by row ---
+    row_words: Dict[int, List[Dict]] = {}
+    for w in words:
+        y_center = w["top"] + w["height"] / 2
+        best = min(rows, key=lambda r: abs(r["y_center"] - y_center))
+        row_words.setdefault(best["index"], []).append(w)
+
+    # --- Compute adaptive gap threshold for group-start detection ---
+    all_gaps: List[float] = []
+    for ri, rw_list in row_words.items():
+        sorted_rw = sorted(rw_list, key=lambda w: w["left"])
+        for i in range(len(sorted_rw) - 1):
+            right = sorted_rw[i]["left"] + sorted_rw[i]["width"]
+            gap = sorted_rw[i + 1]["left"] - right
+            if gap > 0:
+                all_gaps.append(gap)
+
+    if all_gaps:
+        sorted_gaps = sorted(all_gaps)
+        median_gap = sorted_gaps[len(sorted_gaps) // 2]
+        heights = [w["height"] for w in words if w.get("height", 0) > 0]
+        median_h = sorted(heights)[len(heights) // 2] if heights else 25
+
+        # For small word counts (boxes, sub-zones): PaddleOCR returns
+        # multi-word blocks, so ALL inter-word gaps are potential column
+        # boundaries.  Use a low threshold based on word height — any gap
+        # wider than ~1x median word height is a column separator.
+        if len(words) <= 60:
+            gap_threshold = max(median_h * 1.0, 25)
+            logger.info(
+                "alignment columns (small zone): gap_threshold=%.0f "
+                "(median_h=%.0f, %d words, %d gaps: %s)",
+                gap_threshold, median_h, len(words), len(sorted_gaps),
+                [int(g) for g in sorted_gaps[:10]],
+            )
+        else:
+            # Standard approach for large zones (full pages)
+            gap_threshold = max(median_gap * 3, median_h * 1.5, 30)
+            # Cap at 25% of zone width
+            max_gap = zone_w * 0.25
+            if gap_threshold > max_gap > 30:
+                logger.info("alignment columns: capping gap_threshold %.0f -> %.0f (25%% of zone_w=%d)", gap_threshold, max_gap, zone_w)
+                gap_threshold = max_gap
+    else:
+        gap_threshold = 50
+
+    # --- Find group-start positions (left-edges that begin a new column) ---
+    start_positions: List[tuple] = []  # (left_edge, row_index)
+    for ri, rw_list in row_words.items():
+        sorted_rw = sorted(rw_list, key=lambda w: w["left"])
+        # First word in row is always a group start
+        start_positions.append((sorted_rw[0]["left"], ri))
+        for i in range(1, len(sorted_rw)):
+            right_prev = sorted_rw[i - 1]["left"] + sorted_rw[i - 1]["width"]
+            gap = sorted_rw[i]["left"] - right_prev
+            if gap >= gap_threshold:
+                start_positions.append((sorted_rw[i]["left"], ri))
+
+    start_positions.sort(key=lambda x: x[0])
+
+    logger.info(
+        "alignment columns: %d group-start positions from %d words "
+        "(gap_threshold=%.0f, %d rows)",
+        len(start_positions), len(words), gap_threshold, total_rows,
+    )
+
+    if not start_positions:
+        x_min = min(w["left"] for w in words)
+        x_max = max(w["left"] + w["width"] for w in words)
+        return [{"index": 0, "type": "column_text", "x_min": x_min, "x_max": x_max}]
+
+    # --- Cluster group-start positions by X-proximity ---
+    tolerance = max(10, int(zone_w * 0.01))
+    clusters: List[Dict[str, Any]] = []
+    cur_edges = [start_positions[0][0]]
+    cur_rows = {start_positions[0][1]}
+
+    for left, row_idx in start_positions[1:]:
+        if left - cur_edges[-1] <= tolerance:
+            cur_edges.append(left)
+            cur_rows.add(row_idx)
+        else:
+            clusters.append({
+                "mean_x": int(sum(cur_edges) / len(cur_edges)),
+                "min_edge": min(cur_edges),
+                "max_edge": max(cur_edges),
+                "count": len(cur_edges),
+                "distinct_rows": len(cur_rows),
+                "row_coverage": len(cur_rows) / total_rows,
+            })
+            cur_edges = [left]
+            cur_rows = {row_idx}
+    clusters.append({
+        "mean_x": int(sum(cur_edges) / len(cur_edges)),
+        "min_edge": min(cur_edges),
+        "max_edge": max(cur_edges),
+        "count": len(cur_edges),
+        "distinct_rows": len(cur_rows),
+        "row_coverage": len(cur_rows) / total_rows,
+    })
+
+    # --- Filter by row coverage ---
+    # These thresholds must be high enough to avoid false columns in flowing
+    # text (random inter-word gaps) while still detecting real columns in
+    # vocabulary worksheets (which typically have >80% row coverage).
+    MIN_COVERAGE_PRIMARY = 0.35
+    MIN_COVERAGE_SECONDARY = 0.12
+    MIN_WORDS_SECONDARY = 4
+    MIN_DISTINCT_ROWS = 3
+
+    # Content boundary for left-margin detection
+    content_x_min = min(w["left"] for w in words)
+    content_x_max = max(w["left"] + w["width"] for w in words)
+    content_span = content_x_max - content_x_min
+
+    primary = [
+        c for c in clusters
+        if c["row_coverage"] >= MIN_COVERAGE_PRIMARY
+        and c["distinct_rows"] >= MIN_DISTINCT_ROWS
+    ]
+    primary_ids = {id(c) for c in primary}
+    secondary = [
+        c for c in clusters
+        if id(c) not in primary_ids
+        and c["row_coverage"] >= MIN_COVERAGE_SECONDARY
+        and c["count"] >= MIN_WORDS_SECONDARY
+        and c["distinct_rows"] >= MIN_DISTINCT_ROWS
+    ]
+
+    # Tertiary: narrow left-margin columns (page refs, markers) that have
+    # too few rows for secondary but are clearly left-aligned and separated
+    # from the main content.  These appear at the far left or far right and
+    # have a large gap to the nearest significant cluster.
+    used_ids = {id(c) for c in primary} | {id(c) for c in secondary}
+    sig_xs = [c["mean_x"] for c in primary + secondary]
+
+    # Tertiary: clusters that are clearly to the LEFT of the first
+    # significant column (or RIGHT of the last).  If words consistently
+    # start at a position left of the established first column boundary,
+    # they MUST be a separate column — regardless of how few rows they
+    # cover.  The only requirement is a clear spatial gap.
+    MIN_COVERAGE_TERTIARY = 0.02  # at least 1 row effectively
+    tertiary = []
+    for c in clusters:
+        if id(c) in used_ids:
+            continue
+        if c["distinct_rows"] < 1:
+            continue
+        if c["row_coverage"] < MIN_COVERAGE_TERTIARY:
+            continue
+        # Must be near left or right content margin (within 15%)
+        rel_pos = (c["mean_x"] - content_x_min) / content_span if content_span else 0.5
+        if not (rel_pos < 0.15 or rel_pos > 0.85):
+            continue
+        # Must have significant gap to nearest significant cluster
+        if sig_xs:
+            min_dist = min(abs(c["mean_x"] - sx) for sx in sig_xs)
+            if min_dist < max(30, content_span * 0.02):
+                continue
+        tertiary.append(c)
+
+    if tertiary:
+        for c in tertiary:
+            logger.info(
+                "  tertiary (margin) cluster: x=%d (range %d-%d), %d words, %d rows (%.0f%%)",
+                c["mean_x"], c["min_edge"], c["max_edge"],
+                c["count"], c["distinct_rows"], c["row_coverage"] * 100,
+            )
+
+    significant = sorted(primary + secondary + tertiary, key=lambda c: c["mean_x"])
+
+    for c in significant:
+        logger.info(
+            "  significant cluster: x=%d (range %d-%d), %d words, %d rows (%.0f%%)",
+            c["mean_x"], c["min_edge"], c["max_edge"],
+            c["count"], c["distinct_rows"], c["row_coverage"] * 100,
+        )
+    logger.info(
+        "alignment columns: %d clusters, %d primary, %d secondary -> %d significant",
+        len(clusters), len(primary), len(secondary), len(significant),
+    )
+
+    if not significant:
+        # Fallback: single column covering all content
+        x_min = min(w["left"] for w in words)
+        x_max = max(w["left"] + w["width"] for w in words)
+        return [{"index": 0, "type": "column_text", "x_min": x_min, "x_max": x_max}]
+
+    # --- Merge nearby clusters ---
+    merge_distance = max(25, int(zone_w * 0.03))
+    merged = [significant[0].copy()]
+    for s in significant[1:]:
+        if s["mean_x"] - merged[-1]["mean_x"] < merge_distance:
+            prev = merged[-1]
+            total = prev["count"] + s["count"]
+            prev["mean_x"] = (
+                prev["mean_x"] * prev["count"] + s["mean_x"] * s["count"]
+            ) // total
+            prev["count"] = total
+            prev["min_edge"] = min(prev["min_edge"], s["min_edge"])
+            prev["max_edge"] = max(prev["max_edge"], s["max_edge"])
+            prev["distinct_rows"] = max(prev["distinct_rows"], s["distinct_rows"])
+        else:
+            merged.append(s.copy())
+
+    logger.info(
+        "alignment columns: %d after merge (distance=%d)",
+        len(merged), merge_distance,
+    )
+
+    # --- Build column boundaries ---
+    margin = max(5, int(zone_w * 0.005))
+    content_x_min = min(w["left"] for w in words)
+    content_x_max = max(w["left"] + w["width"] for w in words)
+
+    columns: List[Dict[str, Any]] = []
+    for i, cluster in enumerate(merged):
+        x_min = max(content_x_min, cluster["min_edge"] - margin)
+        if i + 1 < len(merged):
+            x_max = merged[i + 1]["min_edge"] - margin
+        else:
+            x_max = content_x_max
+
+        columns.append({
+            "index": i,
+            "type": f"column_{i + 1}" if len(merged) > 1 else "column_text",
+            "x_min": x_min,
+            "x_max": x_max,
+        })
+
+    return columns
+
+
+_MARKER_CHARS = set("*-+#>")
+
+
+def _merge_inline_marker_columns(
+    columns: List[Dict],
+    words: List[Dict],
+) -> List[Dict]:
+    """Merge narrow marker columns (bullets, numbering) into adjacent text.
+
+    Bullet points (*, -) and numbering (1., 2.) create narrow columns
+    at the left edge of a zone.  These are inline markers that indent text,
+    not real separate columns.  Merge them with their right neighbour.
+
+    Does NOT merge columns containing alphabetic words like "to", "in",
+    "der", "die", "das" — those are legitimate content columns.
+    """
+    if len(columns) < 2:
+        return columns
+
+    merged: List[Dict] = []
+    skip: set = set()
+
+    for i, col in enumerate(columns):
+        if i in skip:
+            continue
+
+        # Find words in this column
+        col_words = [
+            w for w in words
+            if col["x_min"] <= w["left"] + w["width"] / 2 < col["x_max"]
+        ]
+        col_width = col["x_max"] - col["x_min"]
+
+        # Narrow column with mostly short words -> MIGHT be inline markers
+        if col_words and col_width < 80:
+            avg_len = sum(len(w.get("text", "")) for w in col_words) / len(col_words)
+            if avg_len <= 2 and i + 1 < len(columns):
+                # Check if words are actual markers (symbols/numbers) vs
+                # real alphabetic words like "to", "in", "der", "die"
+                texts = [(w.get("text") or "").strip() for w in col_words]
+                alpha_count = sum(
+                    1 for t in texts
+                    if t and t[0].isalpha() and t not in _MARKER_CHARS
+                )
+                alpha_ratio = alpha_count / len(texts) if texts else 0
+
+                # If >=50% of words are alphabetic, this is a real column
+                if alpha_ratio >= 0.5:
+                    logger.info(
+                        "  kept narrow column %d (w=%d, avg_len=%.1f, "
+                        "alpha=%.0f%%) -- contains real words",
+                        i, col_width, avg_len, alpha_ratio * 100,
+                    )
+                else:
+                    # Merge into next column
+                    next_col = columns[i + 1].copy()
+                    next_col["x_min"] = col["x_min"]
+                    merged.append(next_col)
+                    skip.add(i + 1)
+                    logger.info(
+                        "  merged inline marker column %d (w=%d, avg_len=%.1f) "
+                        "into column %d",
+                        i, col_width, avg_len, i + 1,
+                    )
+                    continue
+
+        merged.append(col)
+
+    # Re-index
+    for i, col in enumerate(merged):
+        col["index"] = i
+        col["type"] = f"column_{i + 1}" if len(merged) > 1 else "column_text"
+
+    return merged

klausur-service/backend/grid/editor/filters.py

@@ -0,0 +1,402 @@
+"""
+Grid Editor — word/zone filtering, border ghosts, decorative margins, footers.
+
+Split from grid_editor_helpers.py for maintainability.
+All functions are pure computation — no HTTP, DB, or session side effects.
+
+Lizenz: Apache 2.0 (kommerziell nutzbar)
+DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+from typing import Any, Dict, List, Optional, Tuple
+
+logger = logging.getLogger(__name__)
+
+
+def _filter_border_strip_words(words: List[Dict]) -> Tuple[List[Dict], int]:
+    """Remove page-border decoration strip words BEFORE column detection.
+
+    Scans from each page edge inward to find the first significant x-gap
+    (>30 px).  If the edge cluster contains <15 % of total words, those
+    words are removed as border-strip artifacts (alphabet letters,
+    illustration fragments).
+
+    Must run BEFORE ``_build_zone_grid`` so that column detection only
+    sees real content words and doesn't produce inflated row counts.
+    """
+    if len(words) < 10:
+        return words, 0
+
+    sorted_words = sorted(words, key=lambda w: w.get("left", 0))
+    total = len(sorted_words)
+
+    # -- Left-edge scan (running max right-edge) --
+    left_count = 0
+    running_right = 0
+    for gi in range(total - 1):
+        running_right = max(
+            running_right,
+            sorted_words[gi].get("left", 0) + sorted_words[gi].get("width", 0),
+        )
+        if sorted_words[gi + 1].get("left", 0) - running_right > 30:
+            left_count = gi + 1
+            break
+
+    # -- Right-edge scan (running min left) --
+    right_count = 0
+    running_left = sorted_words[-1].get("left", 0)
+    for gi in range(total - 1, 0, -1):
+        running_left = min(running_left, sorted_words[gi].get("left", 0))
+        prev_right = (
+            sorted_words[gi - 1].get("left", 0)
+            + sorted_words[gi - 1].get("width", 0)
+        )
+        if running_left - prev_right > 30:
+            right_count = total - gi
+            break
+
+    # Validate candidate strip: real border decorations are mostly short
+    # words (alphabet letters like "A", "Bb", stray marks).  Multi-word
+    # content like "der Ranzen" or "die Schals" (continuation of German
+    # translations) must NOT be removed.
+    def _is_decorative_strip(candidates: List[Dict]) -> bool:
+        if not candidates:
+            return False
+        short = sum(1 for w in candidates if len((w.get("text") or "").strip()) <= 2)
+        return short / len(candidates) >= 0.45
+
+    strip_ids: set = set()
+    if left_count > 0 and left_count / total < 0.20:
+        candidates = sorted_words[:left_count]
+        if _is_decorative_strip(candidates):
+            strip_ids = {id(w) for w in candidates}
+    elif right_count > 0 and right_count / total < 0.20:
+        candidates = sorted_words[total - right_count:]
+        if _is_decorative_strip(candidates):
+            strip_ids = {id(w) for w in candidates}
+
+    if not strip_ids:
+        return words, 0
+
+    return [w for w in words if id(w) not in strip_ids], len(strip_ids)
+
+
+# Characters that are typically OCR artefacts from box border lines.
+# Intentionally excludes ! (red markers) and . , ; (real punctuation).
+_GRID_GHOST_CHARS = set("|1lI[](){}/\\-\u2014\u2013_~=+")
+
+
+def _filter_border_ghosts(
+    words: List[Dict],
+    boxes: List,
+) -> tuple:
+    """Remove words sitting on box borders that are OCR artefacts.
+
+    Returns (filtered_words, removed_count).
+    """
+    if not boxes or not words:
+        return words, 0
+
+    # Build border bands from detected boxes
+    x_bands: List[tuple] = []
+    y_bands: List[tuple] = []
+    for b in boxes:
+        bt = (
+            b.border_thickness
+            if hasattr(b, "border_thickness")
+            else b.get("border_thickness", 3)
+        )
+        # Skip borderless boxes (images/graphics) -- no border line to produce ghosts
+        if bt == 0:
+            continue
+        bx = b.x if hasattr(b, "x") else b.get("x", 0)
+        by = b.y if hasattr(b, "y") else b.get("y", 0)
+        bw = b.width if hasattr(b, "width") else b.get("w", b.get("width", 0))
+        bh = b.height if hasattr(b, "height") else b.get("h", b.get("height", 0))
+        margin = max(bt * 2, 10) + 6
+        x_bands.append((bx - margin, bx + margin))
+        x_bands.append((bx + bw - margin, bx + bw + margin))
+        y_bands.append((by - margin, by + margin))
+        y_bands.append((by + bh - margin, by + bh + margin))
+
+    def _is_ghost(w: Dict) -> bool:
+        text = (w.get("text") or "").strip()
+        if not text:
+            return False
+        # Check if any word edge (not just center) touches a border band
+        w_left = w["left"]
+        w_right = w["left"] + w["width"]
+        w_top = w["top"]
+        w_bottom = w["top"] + w["height"]
+        on_border = (
+            any(lo <= w_left <= hi or lo <= w_right <= hi for lo, hi in x_bands)
+            or any(lo <= w_top <= hi or lo <= w_bottom <= hi for lo, hi in y_bands)
+        )
+        if not on_border:
+            return False
+        if len(text) == 1 and text in _GRID_GHOST_CHARS:
+            return True
+        return False
+
+    filtered = [w for w in words if not _is_ghost(w)]
+    return filtered, len(words) - len(filtered)
+
+
+def _flatten_word_boxes(cells: List[Dict]) -> List[Dict]:
+    """Extract all word_boxes from cells into a flat list of word dicts."""
+    words: List[Dict] = []
+    for cell in cells:
+        for wb in cell.get("word_boxes") or []:
+            if wb.get("text", "").strip():
+                words.append({
+                    "text": wb["text"],
+                    "left": wb["left"],
+                    "top": wb["top"],
+                    "width": wb["width"],
+                    "height": wb["height"],
+                    "conf": wb.get("conf", 0),
+                })
+    return words
+
+
+def _words_in_zone(
+    words: List[Dict],
+    zone_y: int,
+    zone_h: int,
+    zone_x: int,
+    zone_w: int,
+) -> List[Dict]:
+    """Filter words whose Y-center falls within a zone's bounds."""
+    zone_y_end = zone_y + zone_h
+    zone_x_end = zone_x + zone_w
+    result = []
+    for w in words:
+        cy = w["top"] + w["height"] / 2
+        cx = w["left"] + w["width"] / 2
+        if zone_y <= cy <= zone_y_end and zone_x <= cx <= zone_x_end:
+            result.append(w)
+    return result
+
+
+def _get_content_bounds(words: List[Dict]) -> tuple:
+    """Get content bounds from word positions."""
+    if not words:
+        return 0, 0, 0, 0
+    x_min = min(w["left"] for w in words)
+    y_min = min(w["top"] for w in words)
+    x_max = max(w["left"] + w["width"] for w in words)
+    y_max = max(w["top"] + w["height"] for w in words)
+    return x_min, y_min, x_max - x_min, y_max - y_min
+
+
+def _filter_decorative_margin(
+    words: List[Dict],
+    img_w: int,
+    log: Any,
+    session_id: str,
+) -> Dict[str, Any]:
+    """Remove words that belong to a decorative alphabet strip on a margin.
+
+    Some vocabulary worksheets have a vertical A-Z alphabet graphic along
+    the left or right edge.  OCR reads each letter as an isolated single-
+    character word.  These decorative elements are not content and confuse
+    column/row detection.
+
+    Detection criteria (phase 1 -- find the strip using single-char words):
+      - Words are in the outer 30% of the page (left or right)
+      - Nearly all words are single characters (letters or digits)
+      - At least 8 such words form a vertical strip (>=8 unique Y positions)
+      - Average horizontal spread of the strip is small (< 80px)
+
+    Phase 2 -- once a strip is confirmed, also remove any short word (<=3
+    chars) in the same narrow x-range.  This catches multi-char OCR
+    artifacts like "Vv" that belong to the same decorative element.
+
+    Modifies *words* in place.
+
+    Returns:
+        Dict with 'found' (bool), 'side' (str), 'letters_detected' (int).
+    """
+    no_strip: Dict[str, Any] = {"found": False, "side": "", "letters_detected": 0}
+    if not words or img_w <= 0:
+        return no_strip
+
+    margin_cutoff = img_w * 0.30
+    # Phase 1: find candidate strips using short words (1-2 chars).
+    # OCR often reads alphabet sidebar letters as pairs ("Aa", "Bb")
+    # rather than singles, so accept <=2-char words as strip candidates.
+    left_strip = [
+        w for w in words
+        if len((w.get("text") or "").strip()) <= 2
+        and w["left"] + w.get("width", 0) / 2 < margin_cutoff
+    ]
+    right_strip = [
+        w for w in words
+        if len((w.get("text") or "").strip()) <= 2
+        and w["left"] + w.get("width", 0) / 2 > img_w - margin_cutoff
+    ]
+
+    for strip, side in [(left_strip, "left"), (right_strip, "right")]:
+        if len(strip) < 6:
+            continue
+        # Check vertical distribution: should have many distinct Y positions
+        y_centers = sorted(set(
+            int(w["top"] + w.get("height", 0) / 2) // 20 * 20  # bucket
+            for w in strip
+        ))
+        if len(y_centers) < 6:
+            continue
+        # Check horizontal compactness
+        x_positions = [w["left"] for w in strip]
+        x_min = min(x_positions)
+        x_max = max(x_positions)
+        x_spread = x_max - x_min
+        if x_spread > 80:
+            continue
+
+        # Phase 2: strip confirmed -- also collect short words in same x-range
+        # Expand x-range slightly to catch neighbors (e.g. "Vv" next to "U")
+        strip_x_lo = x_min - 20
+        strip_x_hi = x_max + 60  # word width + tolerance
+        all_strip_words = [
+            w for w in words
+            if len((w.get("text") or "").strip()) <= 3
+            and strip_x_lo <= w["left"] <= strip_x_hi
+            and (w["left"] + w.get("width", 0) / 2 < margin_cutoff
+                 if side == "left"
+                 else w["left"] + w.get("width", 0) / 2 > img_w - margin_cutoff)
+        ]
+
+        strip_set = set(id(w) for w in all_strip_words)
+        before = len(words)
+        words[:] = [w for w in words if id(w) not in strip_set]
+        removed = before - len(words)
+        if removed:
+            log.info(
+                "build-grid session %s: removed %d decorative %s-margin words "
+                "(strip x=%d-%d)",
+                session_id, removed, side, strip_x_lo, strip_x_hi,
+            )
+        return {"found": True, "side": side, "letters_detected": len(strip)}
+
+    return no_strip
+
+
+def _filter_footer_words(
+    words: List[Dict],
+    img_h: int,
+    log: Any,
+    session_id: str,
+) -> Optional[Dict]:
+    """Remove isolated words in the bottom 5% of the page (page numbers).
+
+    Modifies *words* in place and returns a page_number metadata dict
+    if a page number was extracted, or None.
+    """
+    if not words or img_h <= 0:
+        return None
+    footer_y = img_h * 0.95
+    footer_words = [
+        w for w in words
+        if w["top"] + w.get("height", 0) / 2 > footer_y
+    ]
+    if not footer_words:
+        return None
+    # Only remove if footer has very few words (<= 3) with short text
+    total_text = "".join((w.get("text") or "").strip() for w in footer_words)
+    if len(footer_words) <= 3 and len(total_text) <= 10:
+        # Extract page number metadata before removing
+        page_number_info = {
+            "text": total_text.strip(),
+            "y_pct": round(footer_words[0]["top"] / img_h * 100, 1),
+        }
+        # Try to parse as integer
+        digits = "".join(c for c in total_text if c.isdigit())
+        if digits:
+            page_number_info["number"] = int(digits)
+
+        footer_set = set(id(w) for w in footer_words)
+        words[:] = [w for w in words if id(w) not in footer_set]
+        log.info(
+            "build-grid session %s: extracted page number '%s' and removed %d footer words",
+            session_id, total_text, len(footer_words),
+        )
+        return page_number_info
+    return None
+
+
+def _filter_header_junk(
+    words: List[Dict],
+    img_h: int,
+    log: Any,
+    session_id: str,
+) -> None:
+    """Remove OCR junk from header illustrations above the real content.
+
+    Textbook pages often have decorative header graphics (illustrations,
+    icons) that OCR reads as low-confidence junk characters.  Real content
+    typically starts further down the page.
+
+    Algorithm:
+      1. Find the "content start" -- the first Y position where a dense
+         horizontal row of 3+ high-confidence words begins.
+      2. Above that line, remove words with conf < 75 and text <= 3 chars.
+         These are almost certainly OCR artifacts from illustrations.
+
+    Modifies *words* in place.
+    """
+    if not words or img_h <= 0:
+        return
+
+    # --- Find content start: first horizontal row with >=3 high-conf words ---
+    # Sort words by Y
+    sorted_by_y = sorted(words, key=lambda w: w["top"])
+    content_start_y = 0
+    _ROW_TOLERANCE = img_h * 0.02  # words within 2% of page height = same row
+    _MIN_ROW_WORDS = 3
+    _MIN_CONF = 80
+
+    i = 0
+    while i < len(sorted_by_y):
+        row_y = sorted_by_y[i]["top"]
+        # Collect words in this row band
+        row_words = []
+        j = i
+        while j < len(sorted_by_y) and sorted_by_y[j]["top"] - row_y < _ROW_TOLERANCE:
+            row_words.append(sorted_by_y[j])
+            j += 1
+        # Count high-confidence words with real text (> 1 char)
+        high_conf = [
+            w for w in row_words
+            if w.get("conf", 0) >= _MIN_CONF
+            and len((w.get("text") or "").strip()) > 1
+        ]
+        if len(high_conf) >= _MIN_ROW_WORDS:
+            content_start_y = row_y
+            break
+        i = j if j > i else i + 1
+
+    if content_start_y <= 0:
+        return  # no clear content start found
+
+    # --- Remove low-conf short junk above content start ---
+    junk = [
+        w for w in words
+        if w["top"] + w.get("height", 0) < content_start_y
+        and w.get("conf", 0) < 75
+        and len((w.get("text") or "").strip()) <= 3
+    ]
+    if not junk:
+        return
+
+    junk_set = set(id(w) for w in junk)
+    before = len(words)
+    words[:] = [w for w in words if id(w) not in junk_set]
+    removed = before - len(words)
+    if removed:
+        log.info(
+            "build-grid session %s: removed %d header junk words above y=%d "
+            "(content start)",
+            session_id, removed, content_start_y,
+        )

klausur-service/backend/grid/editor/headers.py

@@ -0,0 +1,499 @@
+"""
+Grid Editor — header/heading detection and colspan (merged cell) detection.
+Split from grid_editor_helpers.py.  Pure computation, no HTTP/DB side effects.
+Lizenz: Apache 2.0 | DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+import re
+from typing import Dict, List, Optional
+
+from cv_ocr_engines import _text_has_garbled_ipa
+
+logger = logging.getLogger(__name__)
+
+
+def _detect_heading_rows_by_color(zones_data: List[Dict], img_w: int, img_h: int) -> int:
+    """Detect heading rows by color + height after color annotation.
+
+    A row is a heading if:
+    1. ALL word_boxes have color_name != 'black' (typically 'blue')
+    2. Mean word height > 1.2x median height of all words in the zone
+
+    Detected heading rows are merged into a single spanning cell.
+    Returns count of headings detected.
+    """
+    heading_count = 0
+
+    for z in zones_data:
+        cells = z.get("cells", [])
+        rows = z.get("rows", [])
+        columns = z.get("columns", [])
+        if not cells or not rows or len(columns) < 2:
+            continue
+
+        # Compute median word height across the zone
+        all_heights = []
+        for cell in cells:
+            for wb in cell.get("word_boxes") or []:
+                h = wb.get("height", 0)
+                if h > 0:
+                    all_heights.append(h)
+        if not all_heights:
+            continue
+        all_heights_sorted = sorted(all_heights)
+        median_h = all_heights_sorted[len(all_heights_sorted) // 2]
+
+        heading_row_indices = []
+        for row in rows:
+            if row.get("is_header"):
+                continue  # already detected as header
+            ri = row["index"]
+            row_cells = [c for c in cells if c.get("row_index") == ri]
+            row_wbs = [
+                wb for cell in row_cells
+                for wb in cell.get("word_boxes") or []
+            ]
+            if not row_wbs:
+                continue
+
+            # Condition 1: ALL words are non-black
+            all_colored = all(
+                wb.get("color_name", "black") != "black"
+                for wb in row_wbs
+            )
+            if not all_colored:
+                continue
+
+            # Condition 2: mean height > 1.2x median
+            mean_h = sum(wb.get("height", 0) for wb in row_wbs) / len(row_wbs)
+            if mean_h <= median_h * 1.2:
+                continue
+
+            heading_row_indices.append(ri)
+
+        # Merge heading cells into spanning cells
+        for hri in heading_row_indices:
+            header_cells = [c for c in cells if c.get("row_index") == hri]
+            if len(header_cells) <= 1:
+                # Single cell -- just mark it as heading
+                if header_cells:
+                    header_cells[0]["col_type"] = "heading"
+                    heading_count += 1
+                    # Mark row as header
+                    for row in rows:
+                        if row["index"] == hri:
+                            row["is_header"] = True
+                continue
+
+            # Collect all word_boxes and text from all columns
+            all_wb = []
+            all_text_parts = []
+            for hc in sorted(header_cells, key=lambda c: c["col_index"]):
+                all_wb.extend(hc.get("word_boxes", []))
+                if hc.get("text", "").strip():
+                    all_text_parts.append(hc["text"].strip())
+
+            # Remove all cells for this row, replace with one spanning cell
+            z["cells"] = [c for c in z["cells"] if c.get("row_index") != hri]
+
+            if all_wb:
+                x_min = min(wb["left"] for wb in all_wb)
+                y_min = min(wb["top"] for wb in all_wb)
+                x_max = max(wb["left"] + wb["width"] for wb in all_wb)
+                y_max = max(wb["top"] + wb["height"] for wb in all_wb)
+
+                # Use the actual starting col_index from the first cell
+                first_col = min(hc["col_index"] for hc in header_cells)
+                zone_idx = z.get("zone_index", 0)
+                z["cells"].append({
+                    "cell_id": f"Z{zone_idx}_R{hri:02d}_C{first_col}",
+                    "zone_index": zone_idx,
+                    "row_index": hri,
+                    "col_index": first_col,
+                    "col_type": "heading",
+                    "text": " ".join(all_text_parts),
+                    "confidence": 0.0,
+                    "bbox_px": {"x": x_min, "y": y_min,
+                                "w": x_max - x_min, "h": y_max - y_min},
+                    "bbox_pct": {
+                        "x": round(x_min / img_w * 100, 2) if img_w else 0,
+                        "y": round(y_min / img_h * 100, 2) if img_h else 0,
+                        "w": round((x_max - x_min) / img_w * 100, 2) if img_w else 0,
+                        "h": round((y_max - y_min) / img_h * 100, 2) if img_h else 0,
+                    },
+                    "word_boxes": all_wb,
+                    "ocr_engine": "words_first",
+                    "is_bold": True,
+                })
+
+            # Mark row as header
+            for row in rows:
+                if row["index"] == hri:
+                    row["is_header"] = True
+            heading_count += 1
+
+    return heading_count
+
+
+def _detect_heading_rows_by_single_cell(
+    zones_data: List[Dict], img_w: int, img_h: int,
+) -> int:
+    """Detect heading rows that have only a single content cell.
+
+    Black headings like "Theme" have normal color and height, so they are
+    missed by ``_detect_heading_rows_by_color``.  The distinguishing signal
+    is that they occupy only one column while normal vocabulary rows fill
+    at least 2-3 columns.
+
+    A row qualifies as a heading if:
+    1. It is not already marked as a header/heading.
+    2. It has exactly ONE cell whose col_type starts with ``column_``
+       (excluding column_1 / page_ref which only carries page numbers).
+    3. That single cell is NOT in the last column (continuation/example
+       lines like "2. Ver\u00e4nderung, Wechsel" often sit alone in column_4).
+    4. The text does not start with ``[`` (IPA continuation).
+    5. The zone has >=3 columns and >=5 rows (avoids false positives in
+       tiny zones).
+    6. The majority of rows in the zone have >=2 content cells (ensures
+       we are in a multi-column vocab layout).
+    """
+    heading_count = 0
+
+    for z in zones_data:
+        cells = z.get("cells", [])
+        rows = z.get("rows", [])
+        columns = z.get("columns", [])
+        if len(columns) < 3 or len(rows) < 5:
+            continue
+
+        # Determine the last col_index (example/sentence column)
+        col_indices = sorted(set(c.get("col_index", 0) for c in cells))
+        if not col_indices:
+            continue
+        last_col = col_indices[-1]
+
+        # Count content cells per row (column_* but not column_1/page_ref).
+        # Exception: column_1 cells that contain a dictionary article word
+        # (die/der/das etc.) ARE content -- they appear in dictionary layouts
+        # where the leftmost column holds grammatical articles.
+        _ARTICLE_WORDS = {
+            "die", "der", "das", "dem", "den", "des", "ein", "eine",
+            "the", "a", "an",
+        }
+        row_content_counts: Dict[int, int] = {}
+        for cell in cells:
+            ct = cell.get("col_type", "")
+            if not ct.startswith("column_"):
+                continue
+            if ct == "column_1":
+                ctext = (cell.get("text") or "").strip().lower()
+                if ctext not in _ARTICLE_WORDS:
+                    continue
+            ri = cell.get("row_index", -1)
+            row_content_counts[ri] = row_content_counts.get(ri, 0) + 1
+
+        # Majority of rows must have >=2 content cells
+        multi_col_rows = sum(1 for cnt in row_content_counts.values() if cnt >= 2)
+        if multi_col_rows < len(rows) * 0.4:
+            continue
+
+        # Exclude first and last non-header rows -- these are typically
+        # page numbers or footer text, not headings.
+        non_header_rows = [r for r in rows if not r.get("is_header")]
+        if len(non_header_rows) < 3:
+            continue
+        first_ri = non_header_rows[0]["index"]
+        last_ri = non_header_rows[-1]["index"]
+
+        heading_row_indices = []
+        for row in rows:
+            if row.get("is_header"):
+                continue
+            ri = row["index"]
+            if ri == first_ri or ri == last_ri:
+                continue
+            row_cells = [c for c in cells if c.get("row_index") == ri]
+            content_cells = [
+                c for c in row_cells
+                if c.get("col_type", "").startswith("column_")
+                and (c.get("col_type") != "column_1"
+                     or (c.get("text") or "").strip().lower() in _ARTICLE_WORDS)
+            ]
+            if len(content_cells) != 1:
+                continue
+            cell = content_cells[0]
+            # Not in the last column (continuation/example lines)
+            if cell.get("col_index") == last_col:
+                continue
+            text = (cell.get("text") or "").strip()
+            if not text or text.startswith("["):
+                continue
+            # Continuation lines start with "(" -- e.g. "(usw.)", "(TV-Serie)"
+            if text.startswith("("):
+                continue
+            # Single cell NOT in the first content column is likely a
+            # continuation/overflow line, not a heading.  Real headings
+            # ("Theme 1", "Unit 3: ...") appear in the first or second
+            # content column.
+            first_content_col = col_indices[0] if col_indices else 0
+            if cell.get("col_index", 0) > first_content_col + 1:
+                continue
+            # Skip garbled IPA without brackets (e.g. "ska:f -- ska:vz")
+            # but NOT text with real IPA symbols (e.g. "Theme [\u03b8\u02c8i\u02d0m]")
+            _REAL_IPA_CHARS = set("\u02c8\u02cc\u0259\u026a\u025b\u0252\u028a\u028c\u00e6\u0251\u0254\u0283\u0292\u03b8\u00f0\u014b")
+            if _text_has_garbled_ipa(text) and not any(c in _REAL_IPA_CHARS for c in text):
+                continue
+            # Guard: dictionary section headings are short (1-4 alpha chars
+            # like "A", "Ab", "Zi", "Sch").  Longer text that starts
+            # lowercase is a regular vocabulary word (e.g. "zentral") that
+            # happens to appear alone in its row.
+            alpha_only = re.sub(r'[^a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df\u1e9e]', '', text)
+            if len(alpha_only) > 4 and text[0].islower():
+                continue
+            heading_row_indices.append(ri)
+
+        # Guard: if >25% of eligible rows would become headings, the
+        # heuristic is misfiring (e.g. sparse single-column layout where
+        # most rows naturally have only 1 content cell).
+        eligible_rows = len(non_header_rows) - 2  # minus first/last excluded
+        if eligible_rows > 0 and len(heading_row_indices) > eligible_rows * 0.25:
+            logger.debug(
+                "Skipping single-cell heading detection for zone %s: "
+                "%d/%d rows would be headings (>25%%)",
+                z.get("zone_index"), len(heading_row_indices), eligible_rows,
+            )
+            continue
+
+        for hri in heading_row_indices:
+            header_cells = [c for c in cells if c.get("row_index") == hri]
+            if not header_cells:
+                continue
+
+            # Collect all word_boxes and text
+            all_wb = []
+            all_text_parts = []
+            for hc in sorted(header_cells, key=lambda c: c["col_index"]):
+                all_wb.extend(hc.get("word_boxes", []))
+                if hc.get("text", "").strip():
+                    all_text_parts.append(hc["text"].strip())
+
+            first_col_idx = min(hc["col_index"] for hc in header_cells)
+
+            # Remove old cells for this row, add spanning heading cell
+            z["cells"] = [c for c in z["cells"] if c.get("row_index") != hri]
+
+            if all_wb:
+                x_min = min(wb["left"] for wb in all_wb)
+                y_min = min(wb["top"] for wb in all_wb)
+                x_max = max(wb["left"] + wb["width"] for wb in all_wb)
+                y_max = max(wb["top"] + wb["height"] for wb in all_wb)
+            else:
+                # Fallback to first cell bbox
+                bp = header_cells[0].get("bbox_px", {})
+                x_min = bp.get("x", 0)
+                y_min = bp.get("y", 0)
+                x_max = x_min + bp.get("w", 0)
+                y_max = y_min + bp.get("h", 0)
+
+            zone_idx = z.get("zone_index", 0)
+            z["cells"].append({
+                "cell_id": f"Z{zone_idx}_R{hri:02d}_C{first_col_idx}",
+                "zone_index": zone_idx,
+                "row_index": hri,
+                "col_index": first_col_idx,
+                "col_type": "heading",
+                "text": " ".join(all_text_parts),
+                "confidence": 0.0,
+                "bbox_px": {"x": x_min, "y": y_min,
+                            "w": x_max - x_min, "h": y_max - y_min},
+                "bbox_pct": {
+                    "x": round(x_min / img_w * 100, 2) if img_w else 0,
+                    "y": round(y_min / img_h * 100, 2) if img_h else 0,
+                    "w": round((x_max - x_min) / img_w * 100, 2) if img_w else 0,
+                    "h": round((y_max - y_min) / img_h * 100, 2) if img_h else 0,
+                },
+                "word_boxes": all_wb,
+                "ocr_engine": "words_first",
+                "is_bold": False,
+            })
+
+            for row in rows:
+                if row["index"] == hri:
+                    row["is_header"] = True
+            heading_count += 1
+
+    return heading_count
+
+
+def _detect_header_rows(
+    rows: List[Dict],
+    zone_words: List[Dict],
+    zone_y: int,
+    columns: Optional[List[Dict]] = None,
+    skip_first_row_header: bool = False,
+) -> List[int]:
+    """Detect header rows: first-row heuristic + spanning header detection.
+
+    A "spanning header" is a row whose words stretch across multiple column
+    boundaries (e.g. "Unit4: Bonnie Scotland" centred across 4 columns).
+    """
+    if len(rows) < 2:
+        return []
+
+    headers = []
+
+    if not skip_first_row_header:
+        first_row = rows[0]
+        second_row = rows[1]
+
+        # Gap between first and second row > 0.5x average row height
+        avg_h = sum(r["y_max"] - r["y_min"] for r in rows) / len(rows)
+        gap = second_row["y_min"] - first_row["y_max"]
+        if gap > avg_h * 0.5:
+            headers.append(0)
+
+        # Also check if first row words are taller than average (bold/header text)
+        all_heights = [w["height"] for w in zone_words]
+        median_h = sorted(all_heights)[len(all_heights) // 2] if all_heights else 20
+        first_row_words = [
+            w for w in zone_words
+            if first_row["y_min"] <= w["top"] + w["height"] / 2 <= first_row["y_max"]
+        ]
+        if first_row_words:
+            first_h = max(w["height"] for w in first_row_words)
+            if first_h > median_h * 1.3:
+                if 0 not in headers:
+                    headers.append(0)
+
+    # Note: Spanning-header detection (rows spanning all columns) has been
+    # disabled because it produces too many false positives on vocabulary
+    # worksheets where IPA transcriptions or short entries naturally span
+    # multiple columns with few words.  The first-row heuristic above is
+    # sufficient for detecting real headers.
+
+    return headers
+
+
+def _detect_colspan_cells(
+    zone_words: List[Dict],
+    columns: List[Dict],
+    rows: List[Dict],
+    cells: List[Dict],
+    img_w: int,
+    img_h: int,
+) -> List[Dict]:
+    """Detect and merge cells that span multiple columns (colspan).
+
+    A word-block (PaddleOCR phrase) that extends significantly past a column
+    boundary into the next column indicates a merged cell.  This replaces
+    the incorrectly split cells with a single cell spanning multiple columns.
+
+    Works for both full-page scans and box zones.
+    """
+    if len(columns) < 2 or not zone_words or not rows:
+        return cells
+
+    from cv_words_first import _assign_word_to_row
+
+    # Column boundaries (midpoints between adjacent columns)
+    col_boundaries = []
+    for ci in range(len(columns) - 1):
+        col_boundaries.append((columns[ci]["x_max"] + columns[ci + 1]["x_min"]) / 2)
+
+    def _cols_covered(w_left: float, w_right: float) -> List[int]:
+        """Return list of column indices that a word-block covers."""
+        covered = []
+        for col in columns:
+            col_mid = (col["x_min"] + col["x_max"]) / 2
+            # Word covers a column if it extends past the column's midpoint
+            if w_left < col_mid < w_right:
+                covered.append(col["index"])
+            # Also include column if word starts within it
+            elif col["x_min"] <= w_left < col["x_max"]:
+                covered.append(col["index"])
+        return sorted(set(covered))
+
+    # Group original word-blocks by row
+    row_word_blocks: Dict[int, List[Dict]] = {}
+    for w in zone_words:
+        ri = _assign_word_to_row(w, rows)
+        row_word_blocks.setdefault(ri, []).append(w)
+
+    # For each row, check if any word-block spans multiple columns
+    rows_to_merge: Dict[int, List[Dict]] = {}  # row_index -> list of spanning word-blocks
+
+    for ri, wblocks in row_word_blocks.items():
+        spanning = []
+        for w in wblocks:
+            w_left = w["left"]
+            w_right = w_left + w["width"]
+            covered = _cols_covered(w_left, w_right)
+            if len(covered) >= 2:
+                spanning.append({"word": w, "cols": covered})
+        if spanning:
+            rows_to_merge[ri] = spanning
+
+    if not rows_to_merge:
+        return cells
+
+    # Merge cells for spanning rows
+    new_cells = []
+    for cell in cells:
+        ri = cell.get("row_index", -1)
+        if ri not in rows_to_merge:
+            new_cells.append(cell)
+            continue
+
+        # Check if this cell's column is part of a spanning block
+        ci = cell.get("col_index", -1)
+        is_part_of_span = False
+        for span in rows_to_merge[ri]:
+            if ci in span["cols"]:
+                is_part_of_span = True
+                # Only emit the merged cell for the FIRST column in the span
+                if ci == span["cols"][0]:
+                    # Use the ORIGINAL word-block text (not the split cell texts
+                    # which may have broken words like "euros a" + "nd cents")
+                    orig_word = span["word"]
+                    merged_text = orig_word.get("text", "").strip()
+                    all_wb = [orig_word]
+
+                    # Compute merged bbox
+                    if all_wb:
+                        x_min = min(wb["left"] for wb in all_wb)
+                        y_min = min(wb["top"] for wb in all_wb)
+                        x_max = max(wb["left"] + wb["width"] for wb in all_wb)
+                        y_max = max(wb["top"] + wb["height"] for wb in all_wb)
+                    else:
+                        x_min = y_min = x_max = y_max = 0
+
+                    new_cells.append({
+                        "cell_id": cell["cell_id"],
+                        "row_index": ri,
+                        "col_index": span["cols"][0],
+                        "col_type": "spanning_header",
+                        "colspan": len(span["cols"]),
+                        "text": merged_text,
+                        "confidence": cell.get("confidence", 0),
+                        "bbox_px": {"x": x_min, "y": y_min,
+                                    "w": x_max - x_min, "h": y_max - y_min},
+                        "bbox_pct": {
+                            "x": round(x_min / img_w * 100, 2) if img_w else 0,
+                            "y": round(y_min / img_h * 100, 2) if img_h else 0,
+                            "w": round((x_max - x_min) / img_w * 100, 2) if img_w else 0,
+                            "h": round((y_max - y_min) / img_h * 100, 2) if img_h else 0,
+                        },
+                        "word_boxes": all_wb,
+                        "ocr_engine": cell.get("ocr_engine", ""),
+                        "is_bold": cell.get("is_bold", False),
+                    })
+                    logger.info(
+                        "colspan detected: row %d, cols %s -> merged %d cells (%r)",
+                        ri, span["cols"], len(span["cols"]), merged_text[:50],
+                    )
+                break
+        if not is_part_of_span:
+            new_cells.append(cell)
+
+    return new_cells

klausur-service/backend/grid/editor/helpers.py

@@ -0,0 +1,58 @@
+"""
+Grid Editor helper functions — barrel re-export module.
+
+This file re-exports all public symbols from the split sub-modules
+so that existing ``from grid_editor_helpers import ...`` statements
+continue to work without changes.
+
+Sub-modules:
+  - columns  — column detection, cross-column splitting, marker merging
+  - filters  — word/zone filtering, border ghosts, decorative margins
+  - headers  — header/heading detection, colspan detection
+  - zones    — vertical dividers, zone splitting/merging, zone grid building
+
+Lizenz: Apache 2.0 (kommerziell nutzbar)
+DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+# --- Re-export: columns ---------------------------------------------------
+from .columns import (  # noqa: F401
+    _is_recognized_word,
+    _split_cross_column_words,
+    _cluster_columns_by_alignment,
+    _MARKER_CHARS,
+    _merge_inline_marker_columns,
+)
+
+# --- Re-export: filters ----------------------------------------------------
+from .filters import (  # noqa: F401
+    _filter_border_strip_words,
+    _GRID_GHOST_CHARS,
+    _filter_border_ghosts,
+    _flatten_word_boxes,
+    _words_in_zone,
+    _get_content_bounds,
+    _filter_decorative_margin,
+    _filter_footer_words,
+    _filter_header_junk,
+)
+
+# --- Re-export: headers ----------------------------------------------------
+from .headers import (  # noqa: F401
+    _detect_heading_rows_by_color,
+    _detect_heading_rows_by_single_cell,
+    _detect_header_rows,
+    _detect_colspan_cells,
+)
+
+# --- Re-export: zones -------------------------------------------------------
+from .zones import (  # noqa: F401
+    _PIPE_RE_VSPLIT,
+    _detect_vertical_dividers,
+    _split_zone_at_vertical_dividers,
+    _merge_content_zones_across_boxes,
+    _build_zone_grid,
+)
+
+# --- Re-export from cv_words_first (used by cv_box_layout.py) ---------------
+from cv_words_first import _cluster_rows  # noqa: F401

klausur-service/backend/grid/editor/zones.py

@@ -0,0 +1,389 @@
+"""
+Grid Editor — vertical divider detection, zone splitting/merging, zone grid building.
+
+Split from grid_editor_helpers.py for maintainability.
+All functions are pure computation — no HTTP, DB, or session side effects.
+
+Lizenz: Apache 2.0 (kommerziell nutzbar)
+DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+import re
+from typing import Any, Dict, List, Optional
+
+from cv_vocab_types import PageZone
+from cv_words_first import _cluster_rows, _build_cells
+
+from .columns import (
+    _cluster_columns_by_alignment,
+    _merge_inline_marker_columns,
+    _split_cross_column_words,
+)
+from .headers import (
+    _detect_header_rows,
+    _detect_colspan_cells,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Vertical divider detection and zone splitting
+# ---------------------------------------------------------------------------
+
+_PIPE_RE_VSPLIT = re.compile(r"^\|+$")
+
+
+def _detect_vertical_dividers(
+    words: List[Dict],
+    zone_x: int,
+    zone_w: int,
+    zone_y: int,
+    zone_h: int,
+) -> List[float]:
+    """Detect vertical divider lines from pipe word_boxes at consistent x.
+
+    Returns list of divider x-positions (empty if no dividers found).
+    """
+    if not words or zone_w <= 0 or zone_h <= 0:
+        return []
+
+    # Collect pipe word_boxes
+    pipes = [
+        w for w in words
+        if _PIPE_RE_VSPLIT.match((w.get("text") or "").strip())
+    ]
+    if len(pipes) < 5:
+        return []
+
+    # Cluster pipe x-centers by proximity
+    tolerance = max(15, int(zone_w * 0.02))
+    pipe_xs = sorted(w["left"] + w["width"] / 2 for w in pipes)
+
+    clusters: List[List[float]] = [[pipe_xs[0]]]
+    for x in pipe_xs[1:]:
+        if x - clusters[-1][-1] <= tolerance:
+            clusters[-1].append(x)
+        else:
+            clusters.append([x])
+
+    dividers: List[float] = []
+    for cluster in clusters:
+        if len(cluster) < 5:
+            continue
+        mean_x = sum(cluster) / len(cluster)
+        # Must be between 15% and 85% of zone width
+        rel_pos = (mean_x - zone_x) / zone_w
+        if rel_pos < 0.15 or rel_pos > 0.85:
+            continue
+        # Check vertical coverage: pipes must span >= 50% of zone height
+        cluster_pipes = [
+            w for w in pipes
+            if abs(w["left"] + w["width"] / 2 - mean_x) <= tolerance
+        ]
+        ys = [w["top"] for w in cluster_pipes] + [w["top"] + w["height"] for w in cluster_pipes]
+        y_span = max(ys) - min(ys) if ys else 0
+        if y_span < zone_h * 0.5:
+            continue
+        dividers.append(mean_x)
+
+    return sorted(dividers)
+
+
+def _split_zone_at_vertical_dividers(
+    zone: "PageZone",
+    divider_xs: List[float],
+    vsplit_group_id: int,
+) -> List["PageZone"]:
+    """Split a PageZone at vertical divider positions into sub-zones."""
+    boundaries = [zone.x] + divider_xs + [zone.x + zone.width]
+    hints = []
+    for i in range(len(boundaries) - 1):
+        if i == 0:
+            hints.append("left_of_vsplit")
+        elif i == len(boundaries) - 2:
+            hints.append("right_of_vsplit")
+        else:
+            hints.append("middle_of_vsplit")
+
+    sub_zones = []
+    for i in range(len(boundaries) - 1):
+        x_start = int(boundaries[i])
+        x_end = int(boundaries[i + 1])
+        sub = PageZone(
+            index=0,  # re-indexed later
+            zone_type=zone.zone_type,
+            y=zone.y,
+            height=zone.height,
+            x=x_start,
+            width=x_end - x_start,
+            box=zone.box,
+            image_overlays=zone.image_overlays,
+            layout_hint=hints[i],
+            vsplit_group=vsplit_group_id,
+        )
+        sub_zones.append(sub)
+
+    return sub_zones
+
+
+def _merge_content_zones_across_boxes(
+    zones: List,
+    content_x: int,
+    content_w: int,
+) -> List:
+    """Merge content zones separated by box zones into single zones.
+
+    Box zones become image_overlays on the merged content zone.
+    Pattern: [content, box*, content] -> [merged_content with overlay]
+    Box zones NOT between two content zones stay as standalone zones.
+    """
+    if len(zones) < 3:
+        return zones
+
+    # Group consecutive runs of [content, box+, content]
+    result: List = []
+    i = 0
+    while i < len(zones):
+        z = zones[i]
+        if z.zone_type != "content":
+            result.append(z)
+            i += 1
+            continue
+
+        # Start of a potential merge group: content zone
+        group_contents = [z]
+        group_boxes = []
+        j = i + 1
+        # Absorb [box, content] pairs -- only absorb a box if it's
+        # confirmed to be followed by another content zone.
+        while j < len(zones):
+            if (zones[j].zone_type == "box"
+                    and j + 1 < len(zones)
+                    and zones[j + 1].zone_type == "content"):
+                group_boxes.append(zones[j])
+                group_contents.append(zones[j + 1])
+                j += 2
+            else:
+                break
+
+        if len(group_contents) >= 2 and group_boxes:
+            # Merge: create one large content zone spanning all
+            y_min = min(c.y for c in group_contents)
+            y_max = max(c.y + c.height for c in group_contents)
+            overlays = []
+            for bz in group_boxes:
+                overlay = {
+                    "y": bz.y,
+                    "height": bz.height,
+                    "x": bz.x,
+                    "width": bz.width,
+                }
+                if bz.box:
+                    overlay["box"] = {
+                        "x": bz.box.x,
+                        "y": bz.box.y,
+                        "width": bz.box.width,
+                        "height": bz.box.height,
+                        "confidence": bz.box.confidence,
+                        "border_thickness": bz.box.border_thickness,
+                    }
+                overlays.append(overlay)
+
+            merged = PageZone(
+                index=0,  # re-indexed below
+                zone_type="content",
+                y=y_min,
+                height=y_max - y_min,
+                x=content_x,
+                width=content_w,
+                image_overlays=overlays,
+            )
+            result.append(merged)
+            i = j
+        else:
+            # No merge possible -- emit just the content zone
+            result.append(z)
+            i += 1
+
+    # Re-index zones
+    for idx, z in enumerate(result):
+        z.index = idx
+
+    logger.info(
+        "zone-merge: %d zones -> %d zones after merging across boxes",
+        len(zones), len(result),
+    )
+    return result
+
+
+def _build_zone_grid(
+    zone_words: List[Dict],
+    zone_x: int,
+    zone_y: int,
+    zone_w: int,
+    zone_h: int,
+    zone_index: int,
+    img_w: int,
+    img_h: int,
+    global_columns: Optional[List[Dict]] = None,
+    skip_first_row_header: bool = False,
+) -> Dict[str, Any]:
+    """Build columns, rows, cells for a single zone from its words.
+
+    Args:
+        global_columns: If provided, use these pre-computed column boundaries
+            instead of detecting columns per zone.  Used for content zones so
+            that all content zones (above/between/below boxes) share the same
+            column structure.  Box zones always detect columns independently.
+    """
+    if not zone_words:
+        return {
+            "columns": [],
+            "rows": [],
+            "cells": [],
+            "header_rows": [],
+        }
+
+    # Cluster rows first (needed for column alignment analysis)
+    rows = _cluster_rows(zone_words)
+
+    # Diagnostic logging for small/medium zones (box zones typically have 40-60 words)
+    if len(zone_words) <= 60:
+        import statistics as _st
+        _heights = [w['height'] for w in zone_words if w.get('height', 0) > 0]
+        _med_h = _st.median(_heights) if _heights else 20
+        _y_tol = max(_med_h * 0.5, 5)
+        logger.info(
+            "zone %d row-clustering: %d words, median_h=%.0f, y_tol=%.1f -> %d rows",
+            zone_index, len(zone_words), _med_h, _y_tol, len(rows),
+        )
+        for w in sorted(zone_words, key=lambda ww: (ww['top'], ww['left'])):
+            logger.info(
+                "  zone %d word: y=%d x=%d h=%d w=%d '%s'",
+                zone_index, w['top'], w['left'], w['height'], w['width'],
+                w.get('text', '')[:40],
+            )
+        for r in rows:
+            logger.info(
+                "  zone %d row %d: y_min=%d y_max=%d y_center=%.0f",
+                zone_index, r['index'], r['y_min'], r['y_max'], r['y_center'],
+            )
+
+    # Use global columns if provided, otherwise detect per zone
+    columns = global_columns if global_columns else _cluster_columns_by_alignment(zone_words, zone_w, rows)
+
+    # Merge inline marker columns (bullets, numbering) into adjacent text
+    if not global_columns:
+        columns = _merge_inline_marker_columns(columns, zone_words)
+
+    if not columns or not rows:
+        return {
+            "columns": [],
+            "rows": [],
+            "cells": [],
+            "header_rows": [],
+        }
+
+    # Split word boxes that straddle column boundaries (e.g. "sichzie"
+    # spanning Col 1 + Col 2).  Must happen after column detection and
+    # before cell assignment.
+    # Keep original words for colspan detection (split destroys span info).
+    original_zone_words = zone_words
+    if len(columns) >= 2:
+        zone_words = _split_cross_column_words(zone_words, columns)
+
+    # Build cells
+    cells = _build_cells(zone_words, columns, rows, img_w, img_h)
+
+    # --- Detect colspan (merged cells spanning multiple columns) ---
+    # Uses the ORIGINAL (pre-split) words to detect word-blocks that span
+    # multiple columns.  _split_cross_column_words would have destroyed
+    # this information by cutting words at column boundaries.
+    if len(columns) >= 2:
+        cells = _detect_colspan_cells(original_zone_words, columns, rows, cells, img_w, img_h)
+
+    # Prefix cell IDs with zone index
+    for cell in cells:
+        cell["cell_id"] = f"Z{zone_index}_{cell['cell_id']}"
+        cell["zone_index"] = zone_index
+
+    # Detect header rows (pass columns for spanning header detection)
+    header_rows = _detect_header_rows(rows, zone_words, zone_y, columns,
+                                      skip_first_row_header=skip_first_row_header)
+
+    # Merge cells in spanning header rows into a single col-0 cell
+    if header_rows and len(columns) >= 2:
+        for hri in header_rows:
+            header_cells = [c for c in cells if c["row_index"] == hri]
+            if len(header_cells) <= 1:
+                continue
+            # Collect all word_boxes and text from all columns
+            all_wb = []
+            all_text_parts = []
+            for hc in sorted(header_cells, key=lambda c: c["col_index"]):
+                all_wb.extend(hc.get("word_boxes", []))
+                if hc.get("text", "").strip():
+                    all_text_parts.append(hc["text"].strip())
+            # Remove all header cells, replace with one spanning cell
+            cells = [c for c in cells if c["row_index"] != hri]
+            if all_wb:
+                x_min = min(wb["left"] for wb in all_wb)
+                y_min = min(wb["top"] for wb in all_wb)
+                x_max = max(wb["left"] + wb["width"] for wb in all_wb)
+                y_max = max(wb["top"] + wb["height"] for wb in all_wb)
+                cells.append({
+                    "cell_id": f"R{hri:02d}_C0",
+                    "row_index": hri,
+                    "col_index": 0,
+                    "col_type": "spanning_header",
+                    "text": " ".join(all_text_parts),
+                    "confidence": 0.0,
+                    "bbox_px": {"x": x_min, "y": y_min,
+                                "w": x_max - x_min, "h": y_max - y_min},
+                    "bbox_pct": {
+                        "x": round(x_min / img_w * 100, 2) if img_w else 0,
+                        "y": round(y_min / img_h * 100, 2) if img_h else 0,
+                        "w": round((x_max - x_min) / img_w * 100, 2) if img_w else 0,
+                        "h": round((y_max - y_min) / img_h * 100, 2) if img_h else 0,
+                    },
+                    "word_boxes": all_wb,
+                    "ocr_engine": "words_first",
+                    "is_bold": True,
+                })
+
+    # Convert columns to output format with percentages
+    out_columns = []
+    for col in columns:
+        x_min = col["x_min"]
+        x_max = col["x_max"]
+        out_columns.append({
+            "index": col["index"],
+            "label": col["type"],
+            "x_min_px": round(x_min),
+            "x_max_px": round(x_max),
+            "x_min_pct": round(x_min / img_w * 100, 2) if img_w else 0,
+            "x_max_pct": round(x_max / img_w * 100, 2) if img_w else 0,
+            "bold": False,
+        })
+
+    # Convert rows to output format with percentages
+    out_rows = []
+    for row in rows:
+        out_rows.append({
+            "index": row["index"],
+            "y_min_px": round(row["y_min"]),
+            "y_max_px": round(row["y_max"]),
+            "y_min_pct": round(row["y_min"] / img_h * 100, 2) if img_h else 0,
+            "y_max_pct": round(row["y_max"] / img_h * 100, 2) if img_h else 0,
+            "is_header": row["index"] in header_rows,
+        })
+
+    return {
+        "columns": out_columns,
+        "rows": out_rows,
+        "cells": cells,
+        "header_rows": header_rows,
+        "_raw_columns": columns,  # internal: for propagation to other zones
+    }

klausur-service/backend/grid_build_cell_ops.py

@@ -1,305 +1,4 @@
-"""
+# Backward-compat shim -- module moved to grid/build/cell_ops.py
-Grid Build Cell Ops — Cell-level operations: bullet/artifact removal,
+import importlib as _importlib
-garbled cell cleanup, word-box reordering, and max_columns enforcement.
+import sys as _sys
-
+_sys.modules[__name__] = _importlib.import_module("grid.build.cell_ops")
-Extracted from grid_build_core.py for maintainability.
-"""
-
-import logging
-import re
-from typing import Any, Dict, List, Tuple
-
-from cv_ocr_engines import (
-    _words_to_reading_order_text, _group_words_into_lines, _lookup_ipa,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def _remove_bullets_and_artifacts(zones_data: List[Dict[str, Any]]) -> None:
-    """Remove blue bullet/artifact word_boxes (Step 5i).
-
-    Handles tiny coloured symbols, overlapping word_boxes, duplicate text,
-    and syllable-split word merging.
-    """
-    _ALPHA_WORD_RE = re.compile(r'^[A-Za-z\u00c0-\u024f\-]+[.,;:!?]*$')
-    _REMOVE_SYMBOLS = {'>', '<', '~', '\\', '^', '`', '#', '|', '¬', '¦'}
-
-    bullet_removed = 0
-    for z in zones_data:
-        for cell in z.get("cells", []):
-            wbs = cell.get("word_boxes") or []
-            if len(wbs) < 2:
-                continue
-            to_remove: set = set()
-
-            # Rule (a): tiny coloured symbols
-            for i, wb in enumerate(wbs):
-                cn = wb.get("color_name", "black")
-                if (cn != "black"
-                        and wb.get("width", 0) * wb.get("height", 0) < 200
-                        and wb.get("conf", 100) < 85):
-                    to_remove.add(i)
-
-            # Rule (a2): isolated non-alphanumeric symbols
-            for i, wb in enumerate(wbs):
-                t = (wb.get("text") or "").strip()
-                if t and not re.search(r'[a-zA-Z0-9äöüÄÖÜß]', t) and len(t) <= 2:
-                    if t in _REMOVE_SYMBOLS:
-                        to_remove.add(i)
-
-            # Rule (b) + (c): overlap and duplicate detection
-            to_merge: List[Tuple[int, int]] = []
-            indexed = sorted(enumerate(wbs), key=lambda iw: iw[1].get("left", 0))
-            for p in range(len(indexed) - 1):
-                i1, w1 = indexed[p]
-                i2, w2 = indexed[p + 1]
-                x1s, x1e = w1.get("left", 0), w1.get("left", 0) + w1.get("width", 0)
-                x2s, x2e = w2.get("left", 0), w2.get("left", 0) + w2.get("width", 0)
-                overlap = max(0, min(x1e, x2e) - max(x1s, x2s))
-                min_w = min(w1.get("width", 1), w2.get("width", 1))
-                gap = x2s - x1e
-                overlap_pct = overlap / min_w if min_w > 0 else 0
-
-                if overlap_pct > 0.20:
-                    t1 = (w1.get("text") or "").strip()
-                    t2 = (w2.get("text") or "").strip()
-
-                    # Syllable-split words
-                    if (overlap_pct <= 0.75
-                            and _ALPHA_WORD_RE.match(t1)
-                            and _ALPHA_WORD_RE.match(t2)):
-                        to_merge.append((i1, i2))
-                        continue
-
-                    # High overlap with short prefix
-                    if (overlap_pct > 0.75
-                            and _ALPHA_WORD_RE.match(t1)
-                            and _ALPHA_WORD_RE.match(t2)
-                            and t1.rstrip(".,;:!?").lower() != t2.rstrip(".,;:!?").lower()
-                            and min(len(t1.rstrip(".,;:!?")), len(t2.rstrip(".,;:!?"))) <= 4):
-                        to_merge.append((i1, i2))
-                        continue
-
-                    if overlap_pct <= 0.40:
-                        continue
-
-                    c1 = w1.get("conf", 50)
-                    c2 = w2.get("conf", 50)
-
-                    # Very high overlap: prefer IPA-dictionary word
-                    if overlap_pct > 0.90 and t1.lower() != t2.lower():
-                        in_dict_1 = bool(_lookup_ipa(re.sub(r'[²³¹\d/]', '', t1.lower()), "british")) if t1.isalpha() else False
-                        in_dict_2 = bool(_lookup_ipa(re.sub(r'[²³¹\d/]', '', t2.lower()), "british")) if t2.isalpha() else False
-                        if in_dict_1 and not in_dict_2:
-                            to_remove.add(i2)
-                            continue
-                        elif in_dict_2 and not in_dict_1:
-                            to_remove.add(i1)
-                            continue
-
-                    if c1 < c2:
-                        to_remove.add(i1)
-                    elif c2 < c1:
-                        to_remove.add(i2)
-                    else:
-                        if w1.get("height", 0) > w2.get("height", 0):
-                            to_remove.add(i1)
-                        else:
-                            to_remove.add(i2)
-
-                elif (gap < 6
-                      and w1.get("color_name") == "blue"
-                      and w2.get("color_name") == "blue"
-                      and (w1.get("text") or "").strip() == (w2.get("text") or "").strip()):
-                    c1 = w1.get("conf", 50)
-                    c2 = w2.get("conf", 50)
-                    to_remove.add(i1 if c1 <= c2 else i2)
-
-            # Execute merges first (syllable-split words)
-            if to_merge:
-                merge_parent: Dict[int, int] = {}
-                for mi1, mi2 in to_merge:
-                    actual_mi1 = mi1
-                    while actual_mi1 in merge_parent:
-                        actual_mi1 = merge_parent[actual_mi1]
-                    if actual_mi1 in to_remove or mi2 in to_remove:
-                        continue
-                    if mi2 in merge_parent:
-                        continue
-                    mw1, mw2 = wbs[actual_mi1], wbs[mi2]
-                    mt1 = (mw1.get("text") or "").rstrip(".,;:!?")
-                    mt2 = (mw2.get("text") or "").strip()
-                    merged_text = mt1 + mt2
-                    mx = min(mw1["left"], mw2["left"])
-                    my = min(mw1["top"], mw2["top"])
-                    mr = max(mw1["left"] + mw1["width"],
-                             mw2["left"] + mw2["width"])
-                    mb = max(mw1["top"] + mw1["height"],
-                             mw2["top"] + mw2["height"])
-                    mw1["text"] = merged_text
-                    mw1["left"] = mx
-                    mw1["top"] = my
-                    mw1["width"] = mr - mx
-                    mw1["height"] = mb - my
-                    mw1["conf"] = (mw1.get("conf", 50) + mw2.get("conf", 50)) / 2
-                    to_remove.add(mi2)
-                    merge_parent[mi2] = actual_mi1
-                    bullet_removed -= 1
-
-            if to_remove:
-                bullet_removed += len(to_remove)
-                filtered = [wb for i, wb in enumerate(wbs) if i not in to_remove]
-                cell["word_boxes"] = filtered
-                if not cell.get("_ipa_corrected"):
-                    cell["text"] = _words_to_reading_order_text(filtered)
-
-    if bullet_removed:
-        for z in zones_data:
-            z["cells"] = [c for c in z.get("cells", [])
-                          if (c.get("word_boxes") or c.get("text", "").strip())]
-        logger.info("Step 5i: removed %d bullet/artifact word_boxes", bullet_removed)
-
-
-def _remove_garbled_cells(zones_data: List[Dict[str, Any]]) -> None:
-    """Remove cells whose text is entirely garbled / artifact noise (Step 5j-pre)."""
-    _COMMON_SHORT_WORDS = {
-        "ab", "am", "an", "da", "du", "er", "es", "im", "in", "ja",
-        "ob", "so", "um", "zu", "wo", "je", "oh", "or",
-        "die", "der", "das", "dem", "den", "des", "ein", "und",
-        "auf", "aus", "bei", "bis", "für", "mit", "nur", "von",
-        "a", "i", "an", "as", "at", "be", "by", "do", "go", "he",
-        "if", "in", "is", "it", "me", "my", "no", "of", "oh", "ok",
-        "on", "or", "so", "to", "up", "us", "we",
-        "the", "and", "but", "for", "not",
-    }
-    _PURE_JUNK_RE = re.compile(r'^[\W\d\s]+$')
-    artifact_cells_removed = 0
-
-    for z in zones_data:
-        before = len(z.get("cells", []))
-        kept = []
-        for cell in z.get("cells", []):
-            text = (cell.get("text") or "").strip()
-            core = text.rstrip(".,;:!?'\"")
-            is_artifact = False
-            if not core:
-                is_artifact = True
-            elif _PURE_JUNK_RE.match(core):
-                if core.strip() not in ('=', '(=', '=)', ';', ':', '-', '–', '—'):
-                    is_artifact = True
-            elif len(core) <= 2 and core.lower() not in _COMMON_SHORT_WORDS and not core.isalpha():
-                is_artifact = True
-            elif len(core) <= 3 and core.isupper() and core.lower() not in _COMMON_SHORT_WORDS:
-                is_artifact = True
-            elif (len(core) <= 5 and re.search(r'\d', core) and re.search(r'[A-Za-z]', core)
-                  and not re.match(r'^[pPsS]\.?\d+$', core)):
-                is_artifact = True
-            if is_artifact:
-                kept.append(None)
-            else:
-                kept.append(cell)
-        z["cells"] = [c for c in kept if c is not None]
-        artifact_cells_removed += before - len(z["cells"])
-
-    if artifact_cells_removed:
-        for z in zones_data:
-            cell_ris = {c.get("row_index") for c in z.get("cells", [])}
-            z["rows"] = [r for r in z.get("rows", []) if r["index"] in cell_ris]
-        logger.info("Step 5j-pre: removed %d artifact cells", artifact_cells_removed)
-
-
-def _normalize_word_order(zones_data: List[Dict[str, Any]]) -> None:
-    """Normalise word_box order to reading order (Step 5j)."""
-    wb_reordered = 0
-    for z in zones_data:
-        for cell in z.get("cells", []):
-            wbs = cell.get("word_boxes") or []
-            if len(wbs) < 2:
-                continue
-            lines = _group_words_into_lines(wbs, y_tolerance_px=15)
-            sorted_wbs = [w for line in lines for w in line]
-            if [id(w) for w in sorted_wbs] != [id(w) for w in wbs]:
-                cell["word_boxes"] = sorted_wbs
-                wb_reordered += 1
-    if wb_reordered:
-        logger.info("Step 5j: re-ordered word_boxes in %d cells to reading order", wb_reordered)
-
-
-def _enforce_max_columns(
-    zones_data: List[Dict[str, Any]],
-    max_columns: int,
-) -> None:
-    """Enforce max_columns by merging narrowest columns (Step 5k)."""
-    for z in zones_data:
-        if z.get("zone_type") != "content":
-            continue
-        cols = z.get("columns", [])
-        cells = z.get("cells", [])
-        if len(cols) <= max_columns:
-            continue
-
-        logger.info(
-            "max_columns=%d: zone %s has %d columns -> merging",
-            max_columns, z.get("zone_index"), len(cols),
-        )
-
-        cols_by_width = sorted(cols, key=lambda c: (c.get("x_max_px", 0) - c.get("x_min_px", 0)))
-
-        while len(cols) > max_columns:
-            narrowest = cols_by_width.pop(0)
-            ni = narrowest["index"]
-
-            sorted_by_x = sorted(cols, key=lambda c: c.get("x_min_px", c.get("x_min_pct", 0)))
-            pos = next(i for i, c in enumerate(sorted_by_x) if c["index"] == ni)
-            if pos + 1 < len(sorted_by_x):
-                merge_target = sorted_by_x[pos + 1]
-            elif pos > 0:
-                merge_target = sorted_by_x[pos - 1]
-            else:
-                break
-
-            ti = merge_target["index"]
-
-            merge_target["x_min_px"] = min(
-                merge_target.get("x_min_px", merge_target.get("x_min_pct", 0)),
-                narrowest.get("x_min_px", narrowest.get("x_min_pct", 0)),
-            )
-            merge_target["x_max_px"] = max(
-                merge_target.get("x_max_px", merge_target.get("x_max_pct", 100)),
-                narrowest.get("x_max_px", narrowest.get("x_max_pct", 100)),
-            )
-            if "x_min_pct" in merge_target and "x_min_pct" in narrowest:
-                merge_target["x_min_pct"] = min(merge_target["x_min_pct"], narrowest["x_min_pct"])
-                merge_target["x_max_pct"] = max(merge_target["x_max_pct"], narrowest["x_max_pct"])
-
-            for cell in cells:
-                if cell.get("col_index") == ni:
-                    cell["col_index"] = ti
-                    existing = next(
-                        (c for c in cells if c["col_index"] == ti
-                         and c["row_index"] == cell["row_index"]
-                         and c is not cell),
-                        None,
-                    )
-                    if existing:
-                        existing["text"] = (
-                            (existing.get("text", "") + " " + cell.get("text", "")).strip()
-                        )
-                        existing["word_boxes"] = existing.get("word_boxes", []) + cell.get("word_boxes", [])
-                        cell["_merged"] = True
-
-            z["cells"] = [c for c in cells if not c.get("_merged")]
-            cells = z["cells"]
-            cols.remove(narrowest)
-            cols_by_width = [c for c in cols_by_width if c["index"] != ni]
-
-        # Re-index columns 0..N-1
-        for new_idx, col in enumerate(sorted(cols, key=lambda c: c.get("x_min_px", c.get("x_min_pct", 0)))):
-            old_idx = col["index"]
-            col["index"] = new_idx
-            for cell in cells:
-                if cell.get("col_index") == old_idx:
-                    cell["col_index"] = new_idx
-
-        logger.info("max_columns: zone %s now has %d columns", z.get("zone_index"), len(cols))

klausur-service/backend/grid_build_cleanup.py

@@ -1,390 +1,4 @@
-"""
+# Backward-compat shim -- module moved to grid/build/cleanup.py
-Grid Build Cleanup — Phase 3: Junk row removal, artifact cleanup, pipe
+import importlib as _importlib
-divider removal, connector normalization, border strip detection, and
+import sys as _sys
-alphabet sidebar removal.
+_sys.modules[__name__] = _importlib.import_module("grid.build.cleanup")
-
-Extracted from grid_build_core.py for maintainability.
-"""
-
-import logging
-import re
-from typing import Any, Dict, List
-
-from cv_ocr_engines import _words_to_reading_order_text
-
-logger = logging.getLogger(__name__)
-
-_PIPE_RE = re.compile(r"^\|+$")
-
-
-def _cleanup_zones(
-    zones_data: List[Dict[str, Any]],
-    border_prefiltered: bool,
-    session_id: str,
-) -> bool:
-    """Clean up zone data: remove junk rows, artifacts, pipes, border strips.
-
-    Args:
-        zones_data: List of zone dicts (modified in place).
-        border_prefiltered: Whether border words were already pre-filtered.
-        session_id: For logging.
-
-    Returns:
-        Updated border_prefiltered flag.
-    """
-    _remove_junk_rows(zones_data)
-    _remove_artifact_cells(zones_data)
-    _remove_oversized_word_boxes(zones_data)
-    _remove_pipe_dividers(zones_data)
-    _normalize_connector_columns(zones_data)
-    border_prefiltered = _remove_border_strips(zones_data, border_prefiltered)
-    _remove_alphabet_sidebars(zones_data)
-    return border_prefiltered
-
-
-def _remove_junk_rows(zones_data: List[Dict[str, Any]]) -> None:
-    """Remove rows where ALL cells contain only short, low-confidence text.
-
-    Also removes 'oversized stub' rows and 'scattered debris' rows.
-    """
-    _JUNK_CONF_THRESHOLD = 50
-    _JUNK_MAX_TEXT_LEN = 3
-
-    for z in zones_data:
-        cells = z.get("cells", [])
-        rows = z.get("rows", [])
-        if not cells or not rows:
-            continue
-
-        # Compute median word height across the zone for oversized detection
-        all_wb_heights = [
-            wb["height"]
-            for cell in cells
-            for wb in cell.get("word_boxes") or []
-            if wb.get("height", 0) > 0
-        ]
-        median_wb_h = sorted(all_wb_heights)[len(all_wb_heights) // 2] if all_wb_heights else 28
-
-        junk_row_indices = set()
-        for row in rows:
-            ri = row["index"]
-            row_cells = [c for c in cells if c.get("row_index") == ri]
-            if not row_cells:
-                continue
-
-            row_wbs = [
-                wb for cell in row_cells
-                for wb in cell.get("word_boxes") or []
-            ]
-
-            # Rule 1: ALL word_boxes are low-conf AND short text
-            all_junk = True
-            for wb in row_wbs:
-                text = (wb.get("text") or "").strip()
-                conf = wb.get("conf", 0)
-                if conf >= _JUNK_CONF_THRESHOLD or len(text) > _JUNK_MAX_TEXT_LEN:
-                    all_junk = False
-                    break
-            if all_junk and row_wbs:
-                junk_row_indices.add(ri)
-                continue
-
-            # Rule 2: oversized stub -- <=3 words, short total text,
-            # and word height > 1.8x median
-            if len(row_wbs) <= 3:
-                total_text = "".join((wb.get("text") or "").strip() for wb in row_wbs)
-                max_h = max((wb.get("height", 0) for wb in row_wbs), default=0)
-                has_page_ref = any(
-                    re.match(r'^[pPsS]\.?\s*\d+$', (wb.get("text") or "").strip())
-                    for wb in row_wbs
-                )
-                if len(total_text) <= 5 and max_h > median_wb_h * 1.8 and not has_page_ref:
-                    junk_row_indices.add(ri)
-                    continue
-
-            # Rule 3: scattered debris -- rows with only tiny fragments
-            longest = max(len((wb.get("text") or "").strip()) for wb in row_wbs)
-            if longest <= 2:
-                junk_row_indices.add(ri)
-                continue
-
-        if junk_row_indices:
-            z["cells"] = [c for c in cells if c.get("row_index") not in junk_row_indices]
-            z["rows"] = [r for r in rows if r["index"] not in junk_row_indices]
-            logger.info(
-                "build-grid: removed %d junk rows from zone %d: %s",
-                len(junk_row_indices), z["zone_index"],
-                sorted(junk_row_indices),
-            )
-
-
-def _remove_artifact_cells(zones_data: List[Dict[str, Any]]) -> None:
-    """Remove individual cells with a single very-short, low-conf word."""
-    _ARTIFACT_MAX_LEN = 2
-    _ARTIFACT_CONF_THRESHOLD = 65
-
-    for z in zones_data:
-        cells = z.get("cells", [])
-        if not cells:
-            continue
-        artifact_ids = set()
-        for cell in cells:
-            wbs = cell.get("word_boxes") or []
-            if len(wbs) != 1:
-                continue
-            wb = wbs[0]
-            text = (wb.get("text") or "").strip()
-            conf = wb.get("conf", 100)
-            if len(text) <= _ARTIFACT_MAX_LEN and conf < _ARTIFACT_CONF_THRESHOLD:
-                artifact_ids.add(cell.get("cell_id"))
-        if artifact_ids:
-            z["cells"] = [c for c in cells if c.get("cell_id") not in artifact_ids]
-            logger.info(
-                "build-grid: removed %d artifact cells from zone %d: %s",
-                len(artifact_ids), z.get("zone_index", 0),
-                [c.get("text") for c in cells if c.get("cell_id") in artifact_ids],
-            )
-
-
-def _remove_oversized_word_boxes(zones_data: List[Dict[str, Any]]) -> None:
-    """Remove word_boxes whose height is 3x+ the median (graphic artifacts)."""
-    for z in zones_data:
-        cells = z.get("cells", [])
-        if not cells:
-            continue
-        all_wh = [
-            wb["height"]
-            for cell in cells
-            for wb in cell.get("word_boxes") or []
-            if wb.get("height", 0) > 0
-        ]
-        if not all_wh:
-            continue
-        med_h = sorted(all_wh)[len(all_wh) // 2]
-        oversized_threshold = med_h * 3
-        removed_oversized = 0
-        for cell in cells:
-            wbs = cell.get("word_boxes") or []
-            filtered = [wb for wb in wbs if wb.get("height", 0) <= oversized_threshold]
-            if len(filtered) < len(wbs):
-                removed_oversized += len(wbs) - len(filtered)
-                cell["word_boxes"] = filtered
-                cell["text"] = _words_to_reading_order_text(filtered)
-        if removed_oversized:
-            z["cells"] = [c for c in cells if c.get("word_boxes")]
-            logger.info(
-                "build-grid: removed %d oversized word_boxes (>%dpx) from zone %d",
-                removed_oversized, oversized_threshold, z.get("zone_index", 0),
-            )
-
-
-def _remove_pipe_dividers(zones_data: List[Dict[str, Any]]) -> None:
-    """Remove pipe-character word_boxes (column divider artifacts)."""
-    for z in zones_data:
-        if z.get("vsplit_group") is not None:
-            continue  # pipes already removed before split
-        removed_pipes = 0
-        for cell in z.get("cells", []):
-            wbs = cell.get("word_boxes") or []
-            filtered = [wb for wb in wbs if not _PIPE_RE.match((wb.get("text") or "").strip())]
-            if len(filtered) < len(wbs):
-                removed_pipes += len(wbs) - len(filtered)
-                cell["word_boxes"] = filtered
-                cell["text"] = _words_to_reading_order_text(filtered)
-        if removed_pipes:
-            z["cells"] = [c for c in z.get("cells", []) if (c.get("word_boxes") or c.get("text", "").strip())]
-            logger.info(
-                "build-grid: removed %d pipe-divider word_boxes from zone %d",
-                removed_pipes, z.get("zone_index", 0),
-            )
-
-    # Strip pipe chars ONLY from cell edges (OCR artifacts).
-    # Preserve pipes embedded in words as syllable separators.
-    for z in zones_data:
-        for cell in z.get("cells", []):
-            text = cell.get("text", "")
-            if "|" in text:
-                cleaned = text.strip("|").strip()
-                if cleaned != text.strip():
-                    cell["text"] = cleaned
-
-
-def _normalize_connector_columns(zones_data: List[Dict[str, Any]]) -> None:
-    """Normalize narrow connector columns where OCR appends noise chars.
-
-    In synonym dictionaries a narrow column repeats the same word
-    (e.g. "oder") in every row. OCR sometimes appends noise chars.
-    """
-    for z in zones_data:
-        cols = z.get("columns", [])
-        cells = z.get("cells", [])
-        if not cols or not cells:
-            continue
-        for col in cols:
-            ci = col.get("index")
-            col_cells = [c for c in cells if c.get("col_index") == ci]
-            if len(col_cells) < 3:
-                continue
-            text_counts: Dict[str, int] = {}
-            for c in col_cells:
-                t = (c.get("text") or "").strip()
-                if t:
-                    text_counts[t] = text_counts.get(t, 0) + 1
-            if not text_counts:
-                continue
-            dominant_text = max(text_counts, key=text_counts.get)  # type: ignore[arg-type]
-            dominant_count = text_counts[dominant_text]
-            if len(dominant_text) > 10 or dominant_count < len(col_cells) * 0.6:
-                continue
-            fixed = 0
-            for c in col_cells:
-                t = (c.get("text") or "").strip()
-                if t != dominant_text and t.startswith(dominant_text) and len(t) <= len(dominant_text) + 2:
-                    c["text"] = dominant_text
-                    wbs = c.get("word_boxes") or []
-                    if len(wbs) == 1:
-                        wbs[0]["text"] = dominant_text
-                    fixed += 1
-            if fixed:
-                logger.info(
-                    "build-grid: normalized %d outlier cells in connector column %d "
-                    "(dominant='%s') zone %d",
-                    fixed, ci, dominant_text, z.get("zone_index", 0),
-                )
-
-
-def _remove_border_strips(
-    zones_data: List[Dict[str, Any]],
-    border_prefiltered: bool,
-) -> bool:
-    """Detect and remove page-border decoration strips.
-
-    Returns updated border_prefiltered flag.
-    """
-    border_strip_removed = 0
-    if border_prefiltered:
-        logger.info("Step 4e: skipped (border pre-filter already applied)")
-        return border_prefiltered
-
-    for z in zones_data:
-        cells = z.get("cells", [])
-        if not cells:
-            continue
-        all_wbs_with_cell: list = []
-        for cell in cells:
-            for wb in cell.get("word_boxes") or []:
-                all_wbs_with_cell.append((wb.get("left", 0), wb, cell))
-        if len(all_wbs_with_cell) < 10:
-            continue
-        all_wbs_with_cell.sort(key=lambda t: t[0])
-        total = len(all_wbs_with_cell)
-
-        # -- Left-edge scan --
-        left_strip_count = 0
-        left_gap = 0
-        running_right = 0
-        for gi in range(total - 1):
-            running_right = max(
-                running_right,
-                all_wbs_with_cell[gi][0] + all_wbs_with_cell[gi][1].get("width", 0),
-            )
-            gap = all_wbs_with_cell[gi + 1][0] - running_right
-            if gap > 30:
-                left_strip_count = gi + 1
-                left_gap = gap
-                break
-
-        # -- Right-edge scan --
-        right_strip_count = 0
-        right_gap = 0
-        running_left = all_wbs_with_cell[-1][0]
-        for gi in range(total - 1, 0, -1):
-            running_left = min(running_left, all_wbs_with_cell[gi][0])
-            prev_right = (
-                all_wbs_with_cell[gi - 1][0]
-                + all_wbs_with_cell[gi - 1][1].get("width", 0)
-            )
-            gap = running_left - prev_right
-            if gap > 30:
-                right_strip_count = total - gi
-                right_gap = gap
-                break
-
-        strip_wbs: set = set()
-        strip_side = ""
-        strip_gap = 0
-        strip_count = 0
-        if left_strip_count > 0 and left_strip_count / total < 0.20:
-            strip_side = "left"
-            strip_count = left_strip_count
-            strip_gap = left_gap
-            strip_wbs = {id(t[1]) for t in all_wbs_with_cell[:left_strip_count]}
-        elif right_strip_count > 0 and right_strip_count / total < 0.20:
-            strip_side = "right"
-            strip_count = right_strip_count
-            strip_gap = right_gap
-            strip_wbs = {id(t[1]) for t in all_wbs_with_cell[total - right_strip_count:]}
-
-        if not strip_wbs:
-            continue
-        for cell in cells:
-            wbs = cell.get("word_boxes") or []
-            filtered = [wb for wb in wbs if id(wb) not in strip_wbs]
-            if len(filtered) < len(wbs):
-                border_strip_removed += len(wbs) - len(filtered)
-                cell["word_boxes"] = filtered
-                cell["text"] = _words_to_reading_order_text(filtered)
-        z["cells"] = [c for c in cells
-                      if (c.get("word_boxes") or c.get("text", "").strip())]
-        logger.info(
-            "Step 4e: removed %d border-strip word_boxes (%s) from zone %d "
-            "(gap=%dpx, strip=%d/%d wbs)",
-            border_strip_removed, strip_side, z.get("zone_index", 0),
-            strip_gap, strip_count, total,
-        )
-
-    return border_prefiltered
-
-
-def _remove_alphabet_sidebars(zones_data: List[Dict[str, Any]]) -> None:
-    """Remove decorative edge columns (alphabet sidebar safety net).
-
-    Dictionary pages have A-Z letter sidebars that OCR reads as single-
-    character word_boxes.
-    """
-    for z in zones_data:
-        columns = z.get("columns", [])
-        cells = z.get("cells", [])
-        if len(columns) < 3 or not cells:
-            continue
-        col_cells: Dict[str, List[Dict]] = {}
-        for cell in cells:
-            ct = cell.get("col_type", "")
-            if ct.startswith("column_"):
-                col_cells.setdefault(ct, []).append(cell)
-        col_types_ordered = sorted(col_cells.keys())
-        if len(col_types_ordered) < 3:
-            continue
-        for edge_ct in [col_types_ordered[0], col_types_ordered[-1]]:
-            edge_cells_list = col_cells.get(edge_ct, [])
-            if len(edge_cells_list) < 3:
-                continue
-            texts = [(c.get("text") or "").strip() for c in edge_cells_list]
-            avg_len = sum(len(t) for t in texts) / len(texts)
-            single_char = sum(1 for t in texts if len(t) <= 1)
-            single_ratio = single_char / len(texts)
-            if avg_len > 1.5:
-                continue
-            if single_ratio < 0.7:
-                continue
-            removed_count = len(edge_cells_list)
-            edge_ids = {id(c) for c in edge_cells_list}
-            z["cells"] = [c for c in cells if id(c) not in edge_ids]
-            z["columns"] = [col for col in columns if col.get("col_type") != edge_ct]
-            logger.info(
-                "Step 4f: removed decorative edge column '%s' from zone %d "
-                "(%d cells, avg_len=%.1f, single_char=%.0f%%)",
-                edge_ct, z.get("zone_index", 0), removed_count,
-                avg_len, single_ratio * 100,
-            )
-            break  # only remove one edge per zone

klausur-service/backend/grid_build_core.py

@@ -1,213 +1,4 @@
-"""
+# Backward-compat shim -- module moved to grid/build/core.py
-Grid Build Core — the main _build_grid_core() function.
+import importlib as _importlib
-
+import sys as _sys
-Extracted from grid_editor_api.py for maintainability.
+_sys.modules[__name__] = _importlib.import_module("grid.build.core")
-Takes merged OCR word positions and builds a structured, zone-aware grid.
-
-The function delegates to phase-specific modules:
-- grid_build_zones.py   — image loading, graphic/box detection, zone grids
-- grid_build_cleanup.py — junk rows, artifacts, pipes, border strips
-- grid_build_text_ops.py — color, headings, IPA, page refs
-- grid_build_finalize.py — bullets, max_columns, dictionary, spelling, result
-"""
-
-import logging
-import time
-from typing import Any, Dict, List, Optional
-
-from grid_editor_helpers import (
-    _flatten_word_boxes,
-    _get_content_bounds,
-    _filter_decorative_margin,
-    _filter_footer_words,
-    _filter_header_junk,
-)
-
-from grid_build_zones import _build_zones
-from grid_build_cleanup import _cleanup_zones
-from grid_build_text_ops import _process_text
-from grid_build_finalize import _finalize_grid
-
-logger = logging.getLogger(__name__)
-
-
-async def _build_grid_core(
-    session_id: str,
-    session: dict,
-    *,
-    ipa_mode: str = "auto",
-    syllable_mode: str = "auto",
-    enhance: bool = True,
-    max_columns: Optional[int] = None,
-    min_conf: Optional[int] = None,
-) -> dict:
-    """Core grid building logic — pure computation, no HTTP or DB side effects.
-
-    Args:
-        session_id: Session identifier (for logging and image loading).
-        session: Full session dict from get_session_db().
-        ipa_mode: "auto" (only when English headwords detected), "all"
-            (force IPA on all content columns), "en" (English column only),
-            "de" (German/definition columns only), or "none" (skip entirely).
-        syllable_mode: "auto" (only when original has pipe dividers),
-            "all" (force syllabification on all words), "en" (English only),
-            "de" (German only), or "none" (skip).
-
-    Returns:
-        StructuredGrid result dict.
-
-    Raises:
-        ValueError: If session data is incomplete.
-    """
-    t0 = time.time()
-
-    # ── Phase 1: Input Validation & Word Filtering ──────────────────
-
-    # 1. Validate and load word results
-    word_result = session.get("word_result")
-    if not word_result or not word_result.get("cells"):
-        raise ValueError("No word results found. Run paddle-kombi or rapid-kombi first.")
-
-    img_w = word_result.get("image_width", 0)
-    img_h = word_result.get("image_height", 0)
-    if not img_w or not img_h:
-        raise ValueError("Missing image dimensions in word_result")
-
-    # 2. Flatten all word boxes from cells
-    all_words = _flatten_word_boxes(word_result["cells"])
-    if not all_words:
-        raise ValueError("No word boxes found in cells")
-
-    # 2a-pre. Apply min_conf filter if specified
-    if min_conf and min_conf > 0:
-        before = len(all_words)
-        all_words = [w for w in all_words if w.get('conf', 100) >= min_conf]
-        removed = before - len(all_words)
-        if removed:
-            logger.info("build-grid session %s: min_conf=%d removed %d/%d words",
-                        session_id, min_conf, removed, before)
-
-    logger.info("build-grid session %s: %d words from %d cells (enhance=%s, max_cols=%s, min_conf=%s)",
-                session_id, len(all_words), len(word_result["cells"]),
-                enhance, max_columns, min_conf)
-
-    # 2b. Filter decorative margin columns (alphabet graphics)
-    margin_strip_info = _filter_decorative_margin(all_words, img_w, logger, session_id)
-    margin_strip_detected = margin_strip_info.get("found", False)
-
-    # Read document_category from session
-    document_category = session.get("document_category")
-
-    # 2c. Filter footer rows (page numbers at the very bottom)
-    page_number_info = _filter_footer_words(all_words, img_h, logger, session_id)
-
-    # 2c2. Filter OCR junk from header illustrations
-    _filter_header_junk(all_words, img_h, logger, session_id)
-
-    # 2d. Filter words inside user-defined exclude regions
-    structure_result = session.get("structure_result")
-    exclude_rects = []
-    if structure_result:
-        for er in structure_result.get("exclude_regions", []):
-            exclude_rects.append({
-                "x": er["x"], "y": er["y"],
-                "w": er["w"], "h": er["h"],
-            })
-    if exclude_rects:
-        before = len(all_words)
-        filtered = []
-        for w in all_words:
-            w_cx = w["left"] + w.get("width", 0) / 2
-            w_cy = w["top"] + w.get("height", 0) / 2
-            inside = any(
-                er["x"] <= w_cx <= er["x"] + er["w"]
-                and er["y"] <= w_cy <= er["y"] + er["h"]
-                for er in exclude_rects
-            )
-            if not inside:
-                filtered.append(w)
-        removed = before - len(filtered)
-        if removed:
-            all_words = filtered
-            logger.info(
-                "build-grid session %s: removed %d words inside %d user exclude region(s)",
-                session_id, removed, len(exclude_rects),
-            )
-
-    # 2e. Hard-filter words inside graphic/image regions from structure step
-    graphic_rects: List[Dict[str, int]] = []
-    if structure_result:
-        for g in structure_result.get("graphics", []):
-            graphic_rects.append({
-                "x": g["x"], "y": g["y"],
-                "w": g["w"], "h": g["h"],
-            })
-    if graphic_rects:
-        before = len(all_words)
-        all_words = [
-            w for w in all_words
-            if not any(
-                gr["x"] <= w["left"] + w.get("width", 0) / 2 <= gr["x"] + gr["w"]
-                and gr["y"] <= w["top"] + w.get("height", 0) / 2 <= gr["y"] + gr["h"]
-                for gr in graphic_rects
-            )
-        ]
-        removed = before - len(all_words)
-        if removed:
-            logger.info(
-                "build-grid session %s: hard-removed %d words inside %d structure graphic region(s)",
-                session_id, removed, len(graphic_rects),
-            )
-
-    content_x, content_y, content_w, content_h = _get_content_bounds(all_words)
-
-    # ── Phase 2: Image Processing & Zone Detection ──────────────────
-
-    zone_result = await _build_zones(
-        session_id, session, all_words, graphic_rects,
-        content_x, content_y, content_w, content_h,
-        img_w, img_h,
-    )
-    zones_data = zone_result["zones_data"]
-    boxes_detected = zone_result["boxes_detected"]
-    recovered_count = zone_result["recovered_count"]
-    border_prefiltered = zone_result["border_prefiltered"]
-    img_bgr = zone_result["img_bgr"]
-
-    # ── Phase 3: Junk Removal & Cell Cleanup ────────────────────────
-
-    border_prefiltered = _cleanup_zones(zones_data, border_prefiltered, session_id)
-
-    # ── Phase 4+5a: Color, Headings, IPA, Page Refs ─────────────────
-
-    text_result = _process_text(
-        zones_data, img_bgr, img_w, img_h, ipa_mode, page_number_info,
-    )
-
-    # ── Phase 5b+6: Finalize & Result Assembly ──────────────────────
-
-    duration = time.time() - t0
-
-    result = _finalize_grid(
-        zones_data=zones_data,
-        all_words=all_words,
-        img_bgr=img_bgr,
-        img_w=img_w,
-        img_h=img_h,
-        session_id=session_id,
-        max_columns=max_columns,
-        ipa_mode=ipa_mode,
-        syllable_mode=syllable_mode,
-        en_col_type=text_result["en_col_type"],
-        ipa_target_cols=text_result["ipa_target_cols"],
-        all_content_cols=text_result["all_content_cols"],
-        skip_ipa=text_result["skip_ipa"],
-        document_category=document_category,
-        margin_strip_detected=margin_strip_detected,
-        page_number_info=text_result["page_number_info"],
-        boxes_detected=boxes_detected,
-        recovered_count=recovered_count,
-        duration=duration,
-    )
-
-    return result

klausur-service/backend/grid_build_finalize.py

@@ -1,452 +1,4 @@
-"""
+# Backward-compat shim -- module moved to grid/build/finalize.py
-Grid Build Finalize — Phase 5b+6: Orchestrates cell-level operations,
+import importlib as _importlib
-dictionary detection, syllable dividers, spell checking, empty column
+import sys as _sys
-removal, and result assembly.
+_sys.modules[__name__] = _importlib.import_module("grid.build.finalize")
-
-Extracted from grid_build_core.py for maintainability.
-"""
-
-import logging
-import re
-from typing import Any, Dict, List, Optional
-
-from grid_build_cell_ops import (
-    _remove_bullets_and_artifacts,
-    _remove_garbled_cells,
-    _normalize_word_order,
-    _enforce_max_columns,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def _finalize_grid(
-    zones_data: List[Dict[str, Any]],
-    all_words: List[Dict[str, Any]],
-    img_bgr: Any,
-    img_w: int,
-    img_h: int,
-    session_id: str,
-    max_columns: Optional[int],
-    ipa_mode: str,
-    syllable_mode: str,
-    en_col_type: Optional[str],
-    ipa_target_cols: set,
-    all_content_cols: set,
-    skip_ipa: bool,
-    document_category: Optional[str],
-    margin_strip_detected: bool,
-    page_number_info: Optional[Dict],
-    boxes_detected: int,
-    recovered_count: int,
-    duration: float,
-) -> dict:
-    """Run final processing steps and assemble result dict.
-
-    Handles: bullet removal, artifact cells, word ordering, max_columns,
-    dictionary detection, syllable dividers, spell check, empty columns,
-    internal flag cleanup, and result assembly.
-    """
-    total_cols = sum(len(z.get("columns", [])) for z in zones_data)
-
-    # 5i. Remove blue bullet/artifact word_boxes
-    _remove_bullets_and_artifacts(zones_data)
-
-    # 5j-pre. Remove cells whose text is entirely garbled / artifact noise
-    _remove_garbled_cells(zones_data)
-
-    # 5j. Normalise word_box order to reading order
-    _normalize_word_order(zones_data)
-
-    # 5k. Enforce max_columns by merging narrowest columns
-    if max_columns and max_columns > 0:
-        _enforce_max_columns(zones_data, max_columns)
-
-    # --- Dictionary detection on assembled grid ---
-    dict_detection = _detect_dictionary(
-        zones_data, img_w, img_h, document_category, margin_strip_detected
-    )
-
-    # --- Word-gap merge ---
-    try:
-        from cv_syllable_detect import merge_word_gaps_in_zones
-        merge_word_gaps_in_zones(zones_data, session_id)
-    except Exception as e:
-        logger.warning("Word-gap merge failed: %s", e)
-
-    # --- Pipe auto-correction ---
-    try:
-        from cv_syllable_detect import autocorrect_pipe_artifacts
-        autocorrect_pipe_artifacts(zones_data, session_id)
-    except Exception as e:
-        logger.warning("Pipe autocorrect failed: %s", e)
-
-    # --- Syllable divider insertion ---
-    syllable_insertions = _insert_syllable_dividers(
-        zones_data, img_bgr, session_id, syllable_mode, dict_detection,
-        en_col_type, all_content_cols, total_cols,
-    )
-
-    # --- Split merged words ---
-    _split_merged_words(zones_data, session_id)
-
-    # --- Ensure space before IPA/phonetic brackets ---
-    _fix_ipa_spacing(zones_data)
-
-    # --- SmartSpellChecker ---
-    _run_spell_checker(zones_data, session_id, en_col_type, total_cols)
-
-    # --- Debug log cell counts per column ---
-    for z in zones_data:
-        if z.get("zone_type") == "content":
-            from collections import Counter as _Counter
-            _cc = _Counter(c.get("col_index") for c in z.get("cells", []))
-            _cols = z.get("columns", [])
-            logger.info(
-                "pre-empty-col-removal zone %d: %d cols, cells_per_col=%s",
-                z.get("zone_index", 0), len(_cols), dict(sorted(_cc.items())),
-            )
-
-    # --- Remove empty columns ---
-    _remove_empty_columns(zones_data)
-
-    # Clean up internal flags before returning
-    for z in zones_data:
-        for cell in z.get("cells", []):
-            cell.pop("_ipa_corrected", None)
-
-    # 6. Build result
-    return _assemble_result(
-        zones_data, all_words, img_w, img_h, session_id,
-        ipa_mode, syllable_mode, ipa_target_cols, skip_ipa,
-        dict_detection, page_number_info, boxes_detected,
-        recovered_count, duration, syllable_insertions,
-    )
-
-
-def _detect_dictionary(
-    zones_data: List[Dict[str, Any]],
-    img_w: int,
-    img_h: int,
-    document_category: Optional[str],
-    margin_strip_detected: bool,
-) -> Dict[str, Any]:
-    """Run dictionary detection on the assembled grid."""
-    from cv_layout import _score_dictionary_signals
-    dict_detection: Dict[str, Any] = {"is_dictionary": False, "confidence": 0.0}
-    try:
-        from cv_vocab_types import ColumnGeometry
-        for z in zones_data:
-            zone_cells = z.get("cells", [])
-            zone_cols = z.get("columns", [])
-            if len(zone_cols) < 2 or len(zone_cells) < 10:
-                continue
-            pseudo_geoms = []
-            for col in zone_cols:
-                ci = col["index"]
-                col_cells = [c for c in zone_cells if c.get("col_index") == ci]
-                col_words = []
-                for cell in col_cells:
-                    for wb in cell.get("word_boxes") or []:
-                        col_words.append({
-                            "text": wb.get("text", ""),
-                            "conf": wb.get("conf", 0),
-                            "top": wb.get("top", 0),
-                            "left": wb.get("left", 0),
-                            "height": wb.get("height", 0),
-                            "width": wb.get("width", 0),
-                        })
-                    if not cell.get("word_boxes") and cell.get("text"):
-                        col_words.append({
-                            "text": cell["text"],
-                            "conf": cell.get("confidence", 50),
-                            "top": cell.get("bbox_px", {}).get("y", 0),
-                            "left": cell.get("bbox_px", {}).get("x", 0),
-                            "height": cell.get("bbox_px", {}).get("h", 20),
-                            "width": cell.get("bbox_px", {}).get("w", 50),
-                        })
-                col_w = col.get("x_max_px", 0) - col.get("x_min_px", 0)
-                pseudo_geoms.append(ColumnGeometry(
-                    index=ci, x=col.get("x_min_px", 0), y=0,
-                    width=max(col_w, 1), height=img_h,
-                    word_count=len(col_words), words=col_words,
-                    width_ratio=col_w / max(img_w, 1),
-                ))
-            if len(pseudo_geoms) >= 2:
-                dd = _score_dictionary_signals(
-                    pseudo_geoms,
-                    document_category=document_category,
-                    margin_strip_detected=margin_strip_detected,
-                )
-                if dd["confidence"] > dict_detection["confidence"]:
-                    dict_detection = dd
-    except Exception as e:
-        logger.warning("Dictionary detection failed: %s", e)
-    return dict_detection
-
-
-def _insert_syllable_dividers(
-    zones_data: List[Dict[str, Any]],
-    img_bgr: Any,
-    session_id: str,
-    syllable_mode: str,
-    dict_detection: Dict[str, Any],
-    en_col_type: Optional[str],
-    all_content_cols: set,
-    total_cols: int,
-) -> int:
-    """Insert syllable dividers for dictionary pages. Returns insertion count."""
-    syllable_insertions = 0
-    if syllable_mode == "none" or img_bgr is None:
-        if syllable_mode == "none":
-            for z in zones_data:
-                for cell in z.get("cells", []):
-                    t = cell.get("text", "")
-                    if "|" in t:
-                        cell["text"] = t.replace("|", "")
-        return syllable_insertions
-
-    _syllable_eligible = False
-    if syllable_mode in ("all", "de", "en"):
-        _syllable_eligible = True
-    elif (dict_detection.get("is_dictionary")
-            and dict_detection.get("article_col_index") is not None):
-        _syllable_eligible = True
-
-    _syllable_col_filter: Optional[set] = None
-    if syllable_mode == "en":
-        _syllable_col_filter = {en_col_type} if en_col_type else set()
-    elif syllable_mode == "de":
-        if en_col_type and total_cols >= 3:
-            _syllable_col_filter = all_content_cols - {en_col_type}
-
-    if _syllable_eligible:
-        try:
-            from cv_syllable_detect import insert_syllable_dividers
-            force_syllables = (syllable_mode in ("all", "de", "en"))
-            syllable_insertions = insert_syllable_dividers(
-                zones_data, img_bgr, session_id,
-                force=force_syllables,
-                col_filter=_syllable_col_filter,
-            )
-        except Exception as e:
-            logger.warning("Syllable insertion failed: %s", e)
-
-    return syllable_insertions
-
-
-def _split_merged_words(
-    zones_data: List[Dict[str, Any]],
-    session_id: str,
-) -> None:
-    """Split merged words using dictionary lookup."""
-    try:
-        from cv_review import _try_split_merged_word, _SPELL_AVAILABLE
-        if not _SPELL_AVAILABLE:
-            return
-        split_count = 0
-        for z in zones_data:
-            for cell in z.get("cells", []):
-                text = cell.get("text", "")
-                if not text:
-                    continue
-                parts = []
-                changed = False
-                for token in text.split():
-                    clean = token
-                    bracket_pos = clean.find('[')
-                    suffix_ipa = ""
-                    if bracket_pos > 0:
-                        suffix_ipa = clean[bracket_pos:]
-                        clean = clean[:bracket_pos]
-                    suffix_punct = ""
-                    stripped = clean.rstrip(".,!?;:'\")")
-                    if stripped != clean:
-                        suffix_punct = clean[len(stripped):]
-                        clean = stripped
-                    suffix = suffix_punct + suffix_ipa
-                    contraction = ""
-                    if "'" in clean and clean.index("'") >= 2:
-                        apos_pos = clean.index("'")
-                        contraction = clean[apos_pos:]
-                        clean = clean[:apos_pos]
-                        suffix = contraction + suffix
-                    if len(clean) >= 4 and clean.isalpha():
-                        split = _try_split_merged_word(clean)
-                        if split:
-                            parts.append(split + suffix)
-                            changed = True
-                            continue
-                    parts.append(token)
-                if changed:
-                    cell["text"] = " ".join(parts)
-                    split_count += 1
-        if split_count:
-            logger.info("build-grid session %s: split %d merged words", session_id, split_count)
-    except ImportError:
-        pass
-
-
-def _fix_ipa_spacing(zones_data: List[Dict[str, Any]]) -> None:
-    """Ensure space before IPA/phonetic brackets: 'word[ipa]' -> 'word [ipa]'."""
-    _IPA_NOSPACE_RE = re.compile(r'([a-zA-ZäöüÄÖÜß])(\[[^\]]{2,}\])')
-    for z in zones_data:
-        for cell in z.get("cells", []):
-            text = cell.get("text", "")
-            if text and "[" in text:
-                fixed = _IPA_NOSPACE_RE.sub(r'\1 \2', text)
-                if fixed != text:
-                    cell["text"] = fixed
-
-
-def _run_spell_checker(
-    zones_data: List[Dict[str, Any]],
-    session_id: str,
-    en_col_type: Optional[str],
-    total_cols: int,
-) -> None:
-    """Run SmartSpellChecker on all cells."""
-    try:
-        from smart_spell import SmartSpellChecker
-        _ssc = SmartSpellChecker()
-        spell_fix_count = 0
-
-        for z in zones_data:
-            for cell in z.get("cells", []):
-                text = cell.get("text", "")
-                if not text or not text.strip():
-                    continue
-                ct = cell.get("col_type", "")
-                if not ct.startswith("column_"):
-                    continue
-
-                if total_cols >= 3 and en_col_type:
-                    lang = "en" if ct == en_col_type else "de"
-                elif total_cols <= 2:
-                    lang = "auto"
-                else:
-                    lang = "auto"
-
-                result = _ssc.correct_text(text, lang=lang)
-                if result.changed:
-                    cell["text"] = result.corrected
-                    spell_fix_count += 1
-
-        if spell_fix_count:
-            logger.info(
-                "build-grid session %s: SmartSpellChecker fixed %d cells",
-                session_id, spell_fix_count,
-            )
-    except ImportError:
-        logger.debug("SmartSpellChecker not available in build-grid")
-    except Exception as e:
-        logger.warning("SmartSpellChecker error in build-grid: %s", e)
-
-
-def _remove_empty_columns(zones_data: List[Dict[str, Any]]) -> None:
-    """Remove columns that have no cells assigned."""
-    for z in zones_data:
-        cells = z.get("cells", [])
-        used_col_indices = {c.get("col_index") for c in cells}
-        old_cols = z.get("columns", [])
-        new_cols = [c for c in old_cols if c.get("col_index", c.get("index", -1)) in used_col_indices]
-        if len(new_cols) < len(old_cols):
-            old_to_new = {}
-            for new_i, col in enumerate(new_cols):
-                old_i = col.get("col_index", col.get("index", new_i))
-                old_to_new[old_i] = new_i
-                col["col_index"] = new_i
-                col["index"] = new_i
-                col["label"] = f"column_{new_i + 1}" if len(new_cols) > 1 else "column_text"
-            for cell in cells:
-                old_ci = cell.get("col_index", 0)
-                cell["col_index"] = old_to_new.get(old_ci, old_ci)
-                cell["col_type"] = f"column_{cell['col_index'] + 1}" if len(new_cols) > 1 else "column_text"
-            z["columns"] = new_cols
-
-
-def _assemble_result(
-    zones_data: List[Dict[str, Any]],
-    all_words: List[Dict[str, Any]],
-    img_w: int,
-    img_h: int,
-    session_id: str,
-    ipa_mode: str,
-    syllable_mode: str,
-    ipa_target_cols: set,
-    skip_ipa: bool,
-    dict_detection: Dict[str, Any],
-    page_number_info: Optional[Dict],
-    boxes_detected: int,
-    recovered_count: int,
-    duration: float,
-    syllable_insertions: int,
-) -> dict:
-    """Build the final result dict (Phase 6)."""
-    total_cells = sum(len(z.get("cells", [])) for z in zones_data)
-    total_columns = sum(len(z.get("columns", [])) for z in zones_data)
-    total_rows = sum(len(z.get("rows", [])) for z in zones_data)
-
-    # Collect color statistics
-    color_stats: Dict[str, int] = {}
-    for z in zones_data:
-        for cell in z.get("cells", []):
-            for wb in cell.get("word_boxes", []):
-                cn = wb.get("color_name", "black")
-                color_stats[cn] = color_stats.get(cn, 0) + 1
-
-    # Compute layout metrics
-    all_content_row_heights: List[float] = []
-    for z in zones_data:
-        for row in z.get("rows", []):
-            if not row.get("is_header", False):
-                h = row.get("y_max_px", 0) - row.get("y_min_px", 0)
-                if h > 0:
-                    all_content_row_heights.append(h)
-    avg_row_height = (
-        sum(all_content_row_heights) / len(all_content_row_heights)
-        if all_content_row_heights else 30.0
-    )
-    font_size_suggestion = max(10, int(avg_row_height * 0.6))
-
-    return {
-        "session_id": session_id,
-        "image_width": img_w,
-        "image_height": img_h,
-        "zones": zones_data,
-        "boxes_detected": boxes_detected,
-        "summary": {
-            "total_zones": len(zones_data),
-            "total_columns": total_columns,
-            "total_rows": total_rows,
-            "total_cells": total_cells,
-            "total_words": len(all_words),
-            "recovered_colored": recovered_count,
-            "color_stats": color_stats,
-        },
-        "formatting": {
-            "bold_columns": [],
-            "header_rows": [],
-        },
-        "layout_metrics": {
-            "page_width_px": img_w,
-            "page_height_px": img_h,
-            "avg_row_height_px": round(avg_row_height, 1),
-            "font_size_suggestion_px": font_size_suggestion,
-        },
-        "dictionary_detection": {
-            "is_dictionary": dict_detection.get("is_dictionary", False),
-            "confidence": dict_detection.get("confidence", 0.0),
-            "signals": dict_detection.get("signals", {}),
-            "article_col_index": dict_detection.get("article_col_index"),
-            "headword_col_index": dict_detection.get("headword_col_index"),
-        },
-        "processing_modes": {
-            "ipa_mode": ipa_mode,
-            "syllable_mode": syllable_mode,
-            "ipa_applied": bool(ipa_target_cols) if not skip_ipa else False,
-            "syllables_applied": syllable_insertions > 0,
-        },
-        "page_number": page_number_info,
-        "duration_seconds": round(duration, 2),
-    }

klausur-service/backend/grid_build_text_ops.py

@@ -1,489 +1,4 @@
-"""
+# Backward-compat shim -- module moved to grid/build/text_ops.py
-Grid Build Text Ops — Phase 4+5a: Color annotation, heading detection,
+import importlib as _importlib
-parenthesis fix, IPA phonetic correction, page ref extraction, and
+import sys as _sys
-slash-IPA conversion.
+_sys.modules[__name__] = _importlib.import_module("grid.build.text_ops")
-
-Extracted from grid_build_core.py for maintainability.
-"""
-
-import logging
-import re
-from typing import Any, Dict, List, Optional, Set, Tuple
-
-from cv_color_detect import detect_word_colors
-from cv_ocr_engines import (
-    fix_cell_phonetics, fix_ipa_continuation_cell, _text_has_garbled_ipa,
-    _lookup_ipa,
-)
-from grid_editor_helpers import (
-    _detect_heading_rows_by_color,
-    _detect_heading_rows_by_single_cell,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def _process_text(
-    zones_data: List[Dict[str, Any]],
-    img_bgr: Any,
-    img_w: int,
-    img_h: int,
-    ipa_mode: str,
-    page_number_info: Optional[Dict],
-) -> Dict[str, Any]:
-    """Run color annotation, heading detection, IPA correction, and page refs.
-
-    Args:
-        zones_data: List of zone dicts (modified in place).
-        img_bgr: BGR image array (or None).
-        img_w: Image width.
-        img_h: Image height.
-        ipa_mode: IPA processing mode.
-        page_number_info: Existing page number metadata (may be None).
-
-    Returns:
-        Dict with keys: en_col_type, ipa_target_cols, all_content_cols,
-        skip_ipa, page_number_info.
-    """
-    # 5. Color annotation on final word_boxes in cells
-    if img_bgr is not None:
-        all_wb: List[Dict] = []
-        for z in zones_data:
-            for cell in z.get("cells", []):
-                all_wb.extend(cell.get("word_boxes", []))
-        detect_word_colors(img_bgr, all_wb)
-
-    # 5a. Heading detection by color + height
-    heading_count = _detect_heading_rows_by_color(zones_data, img_w, img_h)
-    if heading_count:
-        logger.info("Detected %d heading rows by color+height", heading_count)
-
-    # 5b. Fix unmatched parentheses in cell text
-    for z in zones_data:
-        for cell in z.get("cells", []):
-            text = cell.get("text", "")
-            if ")" in text and "(" not in text:
-                cell["text"] = "(" + text
-
-    # 5c. IPA phonetic correction
-    all_cells = [cell for z in zones_data for cell in z.get("cells", [])]
-    total_cols = sum(len(z.get("columns", [])) for z in zones_data)
-    en_col_type = None
-    ipa_target_cols: set = set()
-    all_content_cols: set = set()
-    skip_ipa = (ipa_mode == "none")
-
-    # When ipa_mode=none, strip ALL square brackets from ALL content columns
-    if skip_ipa:
-        _SQUARE_BRACKET_RE_NONE = re.compile(r'\s*\[[^\]]+\]')
-        for cell in all_cells:
-            ct = cell.get("col_type", "")
-            if not ct.startswith("column_"):
-                continue
-            text = cell.get("text", "")
-            if "[" in text:
-                stripped = _SQUARE_BRACKET_RE_NONE.sub("", text)
-                if stripped != text:
-                    cell["text"] = stripped.strip()
-                    cell["_ipa_corrected"] = True
-
-    if not skip_ipa and total_cols >= 3:
-        en_col_type, ipa_target_cols, all_content_cols = _run_ipa_correction(
-            all_cells, total_cols, ipa_mode, zones_data
-        )
-    elif not skip_ipa:
-        # Collect all_content_cols even when <3 cols (needed by finalize)
-        for cell in all_cells:
-            ct = cell.get("col_type", "")
-            if ct.startswith("column_") and (cell.get("text") or "").strip():
-                all_content_cols.add(ct)
-
-    # 5e. Heading detection by single-cell rows
-    single_heading_count = _detect_heading_rows_by_single_cell(zones_data, img_w, img_h)
-    if single_heading_count:
-        logger.info("Detected %d heading rows by single-cell heuristic", single_heading_count)
-
-    # 5f. Strip IPA from headings
-    for z in zones_data:
-        for cell in z.get("cells", []):
-            if cell.get("col_type") != "heading":
-                continue
-            text = cell.get("text", "")
-            stripped = re.sub(r'\s*\[[^\]]*\]\s*$', '', text).strip()
-            if stripped and stripped != text:
-                cell["text"] = stripped
-
-    # 5g. Extract page_ref cells and footer rows
-    _extract_page_refs_and_footers(zones_data, page_number_info)
-
-    # 5h. Convert slash-delimited IPA to bracket notation
-    _convert_slash_ipa(zones_data, skip_ipa, en_col_type)
-
-    return {
-        "en_col_type": en_col_type,
-        "ipa_target_cols": ipa_target_cols,
-        "all_content_cols": all_content_cols,
-        "skip_ipa": skip_ipa,
-        "page_number_info": page_number_info,
-    }
-
-
-def _run_ipa_correction(
-    all_cells: List[Dict],
-    total_cols: int,
-    ipa_mode: str,
-    zones_data: List[Dict[str, Any]],
-) -> Tuple[Optional[str], set, set]:
-    """Run IPA correction on cells. Returns (en_col_type, ipa_target_cols, all_content_cols)."""
-    en_col_type = None
-    all_content_cols: set = set()
-
-    # Detect English headword column via IPA signals
-    col_ipa_count: Dict[str, int] = {}
-    for cell in all_cells:
-        ct = cell.get("col_type", "")
-        if not ct.startswith("column_"):
-            continue
-        txt = cell.get("text", "") or ""
-        if txt.strip():
-            all_content_cols.add(ct)
-        if '[' in txt or _text_has_garbled_ipa(txt):
-            col_ipa_count[ct] = col_ipa_count.get(ct, 0) + 1
-    if col_ipa_count:
-        en_col_type = max(col_ipa_count, key=col_ipa_count.get)
-    elif ipa_mode == "all":
-        col_cell_count: Dict[str, int] = {}
-        for cell in all_cells:
-            ct = cell.get("col_type", "")
-            if ct.startswith("column_") and (cell.get("text") or "").strip():
-                col_cell_count[ct] = col_cell_count.get(ct, 0) + 1
-        if col_cell_count:
-            en_col_type = max(col_cell_count, key=col_cell_count.get)
-
-    # Decide which columns to process based on ipa_mode
-    en_ipa_target_cols: set = set()
-    de_ipa_target_cols: set = set()
-    if ipa_mode in ("auto", "en"):
-        if en_col_type:
-            en_ipa_target_cols.add(en_col_type)
-    elif ipa_mode == "de":
-        de_ipa_target_cols = all_content_cols - {en_col_type} if en_col_type else all_content_cols
-    elif ipa_mode == "all":
-        if en_col_type:
-            en_ipa_target_cols.add(en_col_type)
-        de_ipa_target_cols = all_content_cols - en_ipa_target_cols
-
-    # --- Strip IPA from columns NOT in the target set ---
-    _SQUARE_BRACKET_RE = re.compile(r'\s*\[[^\]]+\]')
-    strip_en_ipa = en_col_type and en_col_type not in en_ipa_target_cols
-    if strip_en_ipa or ipa_mode == "none":
-        strip_cols = {en_col_type} if strip_en_ipa and ipa_mode != "none" else all_content_cols
-        for cell in all_cells:
-            ct = cell.get("col_type", "")
-            if ct not in strip_cols:
-                continue
-            text = cell.get("text", "")
-            if "[" in text:
-                stripped = _SQUARE_BRACKET_RE.sub("", text)
-                if stripped != text:
-                    cell["text"] = stripped.strip()
-                    cell["_ipa_corrected"] = True
-
-    # --- English IPA (Britfone + eng_to_ipa) ---
-    if en_ipa_target_cols:
-        for cell in all_cells:
-            ct = cell.get("col_type")
-            if ct in en_ipa_target_cols:
-                cell["_orig_col_type"] = ct
-                cell["col_type"] = "column_en"
-    _pre_ipa = {id(c): c.get("text", "") for c in all_cells}
-    fix_cell_phonetics(all_cells, pronunciation="british")
-    for cell in all_cells:
-        orig = cell.pop("_orig_col_type", None)
-        if orig:
-            cell["col_type"] = orig
-        if cell.get("text", "") != _pre_ipa.get(id(cell), ""):
-            cell["_ipa_corrected"] = True
-
-    # --- German IPA (wiki-pronunciation-dict + epitran) ---
-    if de_ipa_target_cols:
-        from cv_ipa_german import insert_german_ipa
-        insert_german_ipa(all_cells, de_ipa_target_cols)
-
-    ipa_target_cols = en_ipa_target_cols | de_ipa_target_cols
-
-    # Mark cells whose text was changed by IPA correction
-    for cell in all_cells:
-        if cell.get("text", "") != _pre_ipa.get(id(cell), ""):
-            cell["_ipa_corrected"] = True
-
-    # 5d. Fix IPA continuation cells
-    skip_ipa = (ipa_mode == "none")
-    _REAL_IPA_CHARS = set("ˈˌəɪɛɒʊʌæɑɔʃʒθðŋ")
-    ipa_cont_fixed = 0
-    for z in ([] if skip_ipa else zones_data):
-        rows_sorted = sorted(z.get("rows", []), key=lambda r: r["index"])
-        z_cells = z.get("cells", [])
-        for idx, row in enumerate(rows_sorted):
-            if idx == 0:
-                continue
-            ri = row["index"]
-            row_cells = [c for c in z_cells if c.get("row_index") == ri]
-            for cell in row_cells:
-                ct = cell.get("col_type", "")
-                if not ct.startswith("column_"):
-                    continue
-                cell_text = (cell.get("text") or "").strip()
-                if not cell_text:
-                    wb_texts = [w.get("text", "")
-                                for w in cell.get("word_boxes", [])]
-                    cell_text = " ".join(wb_texts).strip()
-                    if not cell_text:
-                        continue
-
-                is_bracketed = (
-                    cell_text.startswith('[') and cell_text.endswith(']')
-                )
-
-                if is_bracketed:
-                    if not _text_has_garbled_ipa(cell_text):
-                        continue
-                    if re.search(r'\[[^\]]*[ˈˌəɪɛɒʊʌæɑɔʃʒθðŋ][^\]]*\]', cell_text):
-                        continue
-                else:
-                    content_cells_in_row = [
-                        c for c in row_cells
-                        if c.get("col_type", "").startswith("column_")
-                        and c.get("col_type") != "column_1"
-                    ]
-                    if len(content_cells_in_row) != 1:
-                        continue
-                    if not _text_has_garbled_ipa(cell_text):
-                        continue
-                    if any(c in _REAL_IPA_CHARS for c in cell_text):
-                        continue
-                    _words_in_text = re.findall(r'[A-Za-zÄÖÜäöüß]{3,}', cell_text)
-                    if len(_words_in_text) >= 3:
-                        continue
-
-                # Find headword in previous row, same column
-                prev_ri = rows_sorted[idx - 1]["index"]
-                prev_same_col = [
-                    c for c in z_cells
-                    if c.get("row_index") == prev_ri
-                    and c.get("col_type") == ct
-                ]
-                if not prev_same_col:
-                    continue
-                prev_text = prev_same_col[0].get("text", "")
-                fixed = fix_ipa_continuation_cell(
-                    cell_text, prev_text, pronunciation="british",
-                )
-                if fixed != cell_text:
-                    cell["text"] = fixed
-                    ipa_cont_fixed += 1
-                    logger.info(
-                        "IPA continuation R%d %s: '%s' -> '%s'",
-                        ri, ct, cell_text, fixed,
-                    )
-    if ipa_cont_fixed:
-        logger.info("Fixed %d IPA continuation cells", ipa_cont_fixed)
-
-    return en_col_type, ipa_target_cols, all_content_cols
-
-
-def _extract_page_refs_and_footers(
-    zones_data: List[Dict[str, Any]],
-    page_number_info: Optional[Dict],
-) -> None:
-    """Extract page_ref cells and footer rows from content zones.
-
-    Modifies zones_data in place. Updates page_number_info if a page number
-    footer is found.
-    """
-    _REAL_IPA_CHARS_SET = set("ˈˌəɪɛɒʊʌæɑɔʃʒθðŋ")
-    _PAGE_REF_RE = re.compile(r'^[pP,]?\s*\.?\s*\d+$')
-    _NUMBER_WORDS = {
-        "one", "two", "three", "four", "five", "six", "seven",
-        "eight", "nine", "ten", "eleven", "twelve", "thirteen",
-        "fourteen", "fifteen", "sixteen", "seventeen", "eighteen",
-        "nineteen", "twenty", "thirty", "forty", "fifty", "sixty",
-        "seventy", "eighty", "ninety", "hundred", "thousand", "and",
-        "einhundert", "zweihundert", "dreihundert", "vierhundert",
-        "und", "zehn", "zwanzig", "dreißig", "vierzig", "fünfzig",
-    }
-
-    for z in zones_data:
-        if z.get("zone_type") != "content":
-            continue
-        cells = z.get("cells", [])
-        rows = z.get("rows", [])
-        if not rows:
-            continue
-
-        # Extract column_1 cells that look like page references
-        page_refs = []
-        page_ref_cell_ids = set()
-        for cell in cells:
-            if cell.get("col_type") != "column_1":
-                continue
-            text = (cell.get("text") or "").strip()
-            if not text:
-                continue
-            if not _PAGE_REF_RE.match(text):
-                continue
-            page_refs.append({
-                "row_index": cell.get("row_index"),
-                "text": text,
-                "bbox_pct": cell.get("bbox_pct", {}),
-            })
-            page_ref_cell_ids.add(cell.get("cell_id"))
-
-        # Detect footer: last non-header row if it has only 1 cell
-        footer_rows = []
-        non_header_rows = [r for r in rows if not r.get("is_header")]
-        if non_header_rows:
-            last_row = non_header_rows[-1]
-            last_ri = last_row["index"]
-            last_cells = [c for c in z["cells"]
-                          if c.get("row_index") == last_ri]
-            if len(last_cells) == 1:
-                text = (last_cells[0].get("text") or "").strip()
-                has_real_ipa = any(c in _REAL_IPA_CHARS_SET for c in text)
-                has_commas = ',' in text
-                text_words = set(text.lower().split())
-                is_written_number = len(text_words) >= 2 and text_words.issubset(_NUMBER_WORDS)
-                is_page_number = len(text) <= 20 or is_written_number
-                if (text and not has_real_ipa and not has_commas
-                        and is_page_number
-                        and last_cells[0].get("col_type") != "heading"):
-                    footer_rows.append({
-                        "row_index": last_ri,
-                        "text": text,
-                        "bbox_pct": last_cells[0].get("bbox_pct", {}),
-                    })
-
-        # Classify footer rows
-        page_number_footers = []
-        other_footers = []
-        for fr in footer_rows:
-            ft = fr["text"].strip()
-            digits = "".join(c for c in ft if c.isdigit())
-            if digits and re.match(r'^[\d\s.]+$', ft):
-                page_number_footers.append(fr)
-            elif ft.lower().split() and set(ft.lower().split()).issubset(_NUMBER_WORDS):
-                page_number_footers.append(fr)
-            else:
-                other_footers.append(fr)
-
-        # Remove page-number footer rows from grid entirely
-        if page_number_footers:
-            pn_ris = {fr["row_index"] for fr in page_number_footers}
-            z["cells"] = [c for c in z["cells"] if c.get("row_index") not in pn_ris]
-            z["rows"] = [r for r in z["rows"] if r["index"] not in pn_ris]
-            pn_text = page_number_footers[0]["text"].strip()
-            pn_digits = "".join(c for c in pn_text if c.isdigit())
-            if not page_number_info:
-                page_number_info = {
-                    "text": pn_text,
-                    "y_pct": page_number_footers[0].get("bbox_pct", {}).get("y", 95),
-                }
-                if pn_digits:
-                    page_number_info["number"] = int(pn_digits)
-
-        # Mark remaining footer rows
-        if other_footers:
-            footer_ris = {fr["row_index"] for fr in other_footers}
-            for r in z["rows"]:
-                if r["index"] in footer_ris:
-                    r["is_footer"] = True
-            for c in z["cells"]:
-                if c.get("row_index") in footer_ris:
-                    c["col_type"] = "footer"
-
-        if page_refs or footer_rows:
-            logger.info(
-                "Extracted %d page_refs + %d footer rows (%d page numbers removed) from zone %d",
-                len(page_refs), len(footer_rows), len(page_number_footers),
-                z.get("zone_index", 0),
-            )
-
-        if page_refs:
-            z["page_refs"] = page_refs
-        if other_footers:
-            z["footer"] = other_footers
-
-
-def _convert_slash_ipa(
-    zones_data: List[Dict[str, Any]],
-    skip_ipa: bool,
-    en_col_type: Optional[str],
-) -> None:
-    """Convert slash-delimited IPA to bracket notation.
-
-    Dictionary-style pages print IPA between slashes: "tiger /'taiga/"
-    """
-    _SLASH_IPA_RE = re.compile(
-        r'(\b[a-zA-Z]+[²³¹]?)\s*'   # headword (capture group 1)
-        r"(/[^/]{2,}/)"              # /ipa/ (capture group 2), min 2 chars
-    )
-    _STANDALONE_SLASH_IPA_RE = re.compile(r'^/([^/]{2,})/')
-    _SLASH_IPA_REJECT_RE = re.compile(r'[\s(),]')
-    slash_ipa_fixed = 0
-
-    for z in ([] if skip_ipa else zones_data):
-        for cell in z.get("cells", []):
-            if en_col_type and cell.get("col_type") != en_col_type:
-                continue
-            text = cell.get("text", "")
-            if "/" not in text:
-                continue
-
-            def _replace_slash_ipa(m: re.Match) -> str:
-                nonlocal slash_ipa_fixed
-                headword = m.group(1)
-                ocr_ipa = m.group(2)
-                inner_raw = ocr_ipa.strip("/").strip()
-                if _SLASH_IPA_REJECT_RE.search(inner_raw):
-                    return m.group(0)
-                clean_hw = re.sub(r'[²³¹\d]', '', headword).strip()
-                ipa = _lookup_ipa(clean_hw, "british") if clean_hw else None
-                if ipa:
-                    slash_ipa_fixed += 1
-                    return f"{headword} [{ipa}]"
-                inner = inner_raw.lstrip("'").strip()
-                if inner:
-                    slash_ipa_fixed += 1
-                    return f"{headword} [{inner}]"
-                return m.group(0)
-
-            new_text = _SLASH_IPA_RE.sub(_replace_slash_ipa, text)
-
-            _AFTER_BRACKET_SLASH = re.compile(r'(?<=\])\s*(/[^/]{2,}/)')
-
-            def _replace_trailing_slash(m: re.Match) -> str:
-                nonlocal slash_ipa_fixed
-                inner = m.group(1).strip("/").strip().lstrip("'").strip()
-                if _SLASH_IPA_REJECT_RE.search(inner):
-                    return m.group(0)
-                if inner:
-                    slash_ipa_fixed += 1
-                    return f" [{inner}]"
-                return m.group(0)
-            new_text = _AFTER_BRACKET_SLASH.sub(_replace_trailing_slash, new_text)
-
-            if new_text == text:
-                m = _STANDALONE_SLASH_IPA_RE.match(text)
-                if m:
-                    inner = m.group(1).strip()
-                    if not _SLASH_IPA_REJECT_RE.search(inner):
-                        inner = inner.lstrip("'").strip()
-                        if inner:
-                            new_text = "[" + inner + "]" + text[m.end():]
-                            slash_ipa_fixed += 1
-
-            if new_text != text:
-                cell["text"] = new_text
-
-    if slash_ipa_fixed:
-        logger.info("Step 5h: converted %d slash-IPA to bracket notation", slash_ipa_fixed)

klausur-service/backend/grid_build_zones.py

@@ -1,462 +1,4 @@
-"""
+# Backward-compat shim -- module moved to grid/build/zones.py
-Grid Build Zones — Phase 2: Image processing, graphic detection, box/zone
+import importlib as _importlib
-detection and zone-aware grid building.
+import sys as _sys
-
+_sys.modules[__name__] = _importlib.import_module("grid.build.zones")
-Extracted from grid_build_core.py for maintainability.
-"""
-
-import logging
-from typing import Any, Dict, List, Optional
-
-import cv2
-import numpy as np
-
-from cv_box_detect import detect_boxes, split_page_into_zones
-from cv_graphic_detect import detect_graphic_elements
-from cv_color_detect import recover_colored_text
-from cv_vocab_types import PageZone
-from ocr_pipeline_session_store import get_session_image
-
-from grid_editor_helpers import (
-    _filter_border_strip_words,
-    _filter_border_ghosts,
-    _words_in_zone,
-    _PIPE_RE_VSPLIT,
-    _detect_vertical_dividers,
-    _split_zone_at_vertical_dividers,
-    _merge_content_zones_across_boxes,
-    _build_zone_grid,
-)
-
-logger = logging.getLogger(__name__)
-
-
-async def _build_zones(
-    session_id: str,
-    session: dict,
-    all_words: List[Dict[str, Any]],
-    graphic_rects: List[Dict[str, int]],
-    content_x: int,
-    content_y: int,
-    content_w: int,
-    content_h: int,
-    img_w: int,
-    img_h: int,
-) -> Dict[str, Any]:
-    """Load image, detect graphics/boxes, build zone-aware grids.
-
-    Returns a dict with keys:
-        zones_data, boxes_detected, recovered_count, border_prefiltered,
-        img_bgr, all_words (modified in-place but returned for clarity).
-    """
-    zones_data: List[Dict[str, Any]] = []
-    boxes_detected = 0
-    recovered_count = 0
-    border_prefiltered = False
-    img_bgr = None
-
-    # 3. Load image for box detection
-    img_png = await get_session_image(session_id, "cropped")
-    if not img_png:
-        img_png = await get_session_image(session_id, "dewarped")
-    if not img_png:
-        img_png = await get_session_image(session_id, "original")
-
-    if img_png:
-        # Decode image for color detection + box detection
-        arr = np.frombuffer(img_png, dtype=np.uint8)
-        img_bgr = cv2.imdecode(arr, cv2.IMREAD_COLOR)
-
-        if img_bgr is not None:
-            # --- 3a. Detect graphic/image regions via CV and hard-filter ---
-            sig_words = [w for w in all_words if len((w.get("text") or "").strip()) >= 3]
-            fresh_graphics = detect_graphic_elements(img_bgr, sig_words)
-            if fresh_graphics:
-                fresh_rects = [
-                    {"x": g.x, "y": g.y, "w": g.width, "h": g.height}
-                    for g in fresh_graphics
-                ]
-                graphic_rects.extend(fresh_rects)
-                logger.info(
-                    "build-grid session %s: detected %d graphic region(s) via CV",
-                    session_id, len(fresh_graphics),
-                )
-                # Hard-filter words inside newly detected graphic regions
-                before = len(all_words)
-                all_words[:] = [
-                    w for w in all_words
-                    if not any(
-                        gr["x"] <= w["left"] + w.get("width", 0) / 2 <= gr["x"] + gr["w"]
-                        and gr["y"] <= w["top"] + w.get("height", 0) / 2 <= gr["y"] + gr["h"]
-                        for gr in fresh_rects
-                    )
-                ]
-                removed = before - len(all_words)
-                if removed:
-                    logger.info(
-                        "build-grid session %s: hard-removed %d words inside %d fresh graphic region(s)",
-                        session_id, removed, len(fresh_rects),
-                    )
-
-            # --- Recover colored text that OCR missed (before grid building) ---
-            recovered = recover_colored_text(img_bgr, all_words)
-            if recovered and graphic_rects:
-                # Filter recovered chars inside graphic regions
-                recovered = [
-                    r for r in recovered
-                    if not any(
-                        gr["x"] <= r["left"] + r.get("width", 0) / 2 <= gr["x"] + gr["w"]
-                        and gr["y"] <= r["top"] + r.get("height", 0) / 2 <= gr["y"] + gr["h"]
-                        for gr in graphic_rects
-                    )
-                ]
-            if recovered:
-                recovered_count = len(recovered)
-                all_words.extend(recovered)
-                logger.info(
-                    "build-grid session %s: +%d recovered colored words",
-                    session_id, recovered_count,
-                )
-
-            # Detect bordered boxes
-            boxes = detect_boxes(
-                img_bgr,
-                content_x=content_x,
-                content_w=content_w,
-                content_y=content_y,
-                content_h=content_h,
-            )
-            boxes_detected = len(boxes)
-
-            if boxes:
-                # Filter border ghost words before grid building
-                all_words_new, ghost_count = _filter_border_ghosts(all_words, boxes)
-                if ghost_count:
-                    all_words[:] = all_words_new
-                    logger.info(
-                        "build-grid session %s: removed %d border ghost words",
-                        session_id, ghost_count,
-                    )
-
-                # Split page into zones
-                page_zones = split_page_into_zones(
-                    content_x, content_y, content_w, content_h, boxes
-                )
-
-                # Merge content zones separated by box zones
-                page_zones = _merge_content_zones_across_boxes(
-                    page_zones, content_x, content_w
-                )
-
-                # 3b. Detect vertical dividers and split content zones
-                page_zones, border_prefiltered_vd = _detect_and_split_vertical_dividers(
-                    page_zones, all_words
-                )
-
-                # --- First pass: build grids per zone independently ---
-                zone_grids = _build_grids_per_zone(
-                    page_zones, all_words, img_w, img_h
-                )
-                border_prefiltered = border_prefiltered or any(
-                    zg.get("_border_prefiltered") for zg in zone_grids
-                )
-
-                # --- Second pass: merge column boundaries from all content zones ---
-                _merge_content_zone_columns(
-                    zone_grids, all_words, content_w, img_w, img_h, session_id
-                )
-
-                # --- Build zones_data from zone_grids ---
-                for zg in zone_grids:
-                    pz = zg["pz"]
-                    grid = zg["grid"]
-                    grid.pop("_raw_columns", None)
-
-                    zone_entry: Dict[str, Any] = {
-                        "zone_index": pz.index,
-                        "zone_type": pz.zone_type,
-                        "bbox_px": {
-                            "x": pz.x, "y": pz.y,
-                            "w": pz.width, "h": pz.height,
-                        },
-                        "bbox_pct": {
-                            "x": round(pz.x / img_w * 100, 2) if img_w else 0,
-                            "y": round(pz.y / img_h * 100, 2) if img_h else 0,
-                            "w": round(pz.width / img_w * 100, 2) if img_w else 0,
-                            "h": round(pz.height / img_h * 100, 2) if img_h else 0,
-                        },
-                        "border": None,
-                        "word_count": len(zg["words"]),
-                        **grid,
-                    }
-
-                    if pz.box:
-                        zone_entry["border"] = {
-                            "thickness": pz.box.border_thickness,
-                            "confidence": pz.box.confidence,
-                        }
-
-                    if pz.image_overlays:
-                        zone_entry["image_overlays"] = pz.image_overlays
-
-                    if pz.layout_hint:
-                        zone_entry["layout_hint"] = pz.layout_hint
-                    if pz.vsplit_group is not None:
-                        zone_entry["vsplit_group"] = pz.vsplit_group
-
-                    zones_data.append(zone_entry)
-
-    # 4. Fallback: no boxes detected -> single zone with all words
-    if not zones_data:
-        before = len(all_words)
-        filtered_words = [
-            w for w in all_words
-            if not (w.get("recovered") and len(w.get("text", "").strip()) <= 2)
-        ]
-        removed = before - len(filtered_words)
-        if removed:
-            logger.info(
-                "build-grid session %s: filtered %d recovered artifacts (fallback zone)",
-                session_id, removed,
-            )
-        filtered_words, bs_removed = _filter_border_strip_words(filtered_words)
-        if bs_removed:
-            border_prefiltered = True
-            logger.info(
-                "build-grid session %s: pre-filtered %d border-strip words",
-                session_id, bs_removed,
-            )
-        grid = _build_zone_grid(
-            filtered_words, content_x, content_y, content_w, content_h,
-            0, img_w, img_h,
-        )
-        grid.pop("_raw_columns", None)
-        zones_data.append({
-            "zone_index": 0,
-            "zone_type": "content",
-            "bbox_px": {
-                "x": content_x, "y": content_y,
-                "w": content_w, "h": content_h,
-            },
-            "bbox_pct": {
-                "x": round(content_x / img_w * 100, 2) if img_w else 0,
-                "y": round(content_y / img_h * 100, 2) if img_h else 0,
-                "w": round(content_w / img_w * 100, 2) if img_w else 0,
-                "h": round(content_h / img_h * 100, 2) if img_h else 0,
-            },
-            "border": None,
-            "word_count": len(all_words),
-            **grid,
-        })
-
-    return {
-        "zones_data": zones_data,
-        "boxes_detected": boxes_detected,
-        "recovered_count": recovered_count,
-        "border_prefiltered": border_prefiltered,
-        "img_bgr": img_bgr,
-    }
-
-
-def _detect_and_split_vertical_dividers(
-    page_zones: List[PageZone],
-    all_words: List[Dict[str, Any]],
-) -> tuple:
-    """Detect vertical dividers and split content zones.
-
-    Returns (expanded_zones, border_prefiltered_from_vsplit).
-    """
-    vsplit_group_counter = 0
-    expanded_zones: List = []
-    for pz in page_zones:
-        if pz.zone_type != "content":
-            expanded_zones.append(pz)
-            continue
-        zone_words = _words_in_zone(
-            all_words, pz.y, pz.height, pz.x, pz.width
-        )
-        divider_xs = _detect_vertical_dividers(
-            zone_words, pz.x, pz.width, pz.y, pz.height
-        )
-        if divider_xs:
-            sub_zones = _split_zone_at_vertical_dividers(
-                pz, divider_xs, vsplit_group_counter
-            )
-            expanded_zones.extend(sub_zones)
-            vsplit_group_counter += 1
-            # Remove pipe words so they don't appear in sub-zones
-            pipe_ids = set(
-                id(w) for w in zone_words
-                if _PIPE_RE_VSPLIT.match((w.get("text") or "").strip())
-            )
-            all_words[:] = [w for w in all_words if id(w) not in pipe_ids]
-            logger.info(
-                "build-grid: vertical split zone %d at x=%s -> %d sub-zones",
-                pz.index, [int(x) for x in divider_xs], len(sub_zones),
-            )
-        else:
-            expanded_zones.append(pz)
-    # Re-index zones
-    for i, pz in enumerate(expanded_zones):
-        pz.index = i
-    return expanded_zones, False
-
-
-def _build_grids_per_zone(
-    page_zones: List[PageZone],
-    all_words: List[Dict[str, Any]],
-    img_w: int,
-    img_h: int,
-) -> List[Dict[str, Any]]:
-    """Build grids for each zone independently (first pass)."""
-    zone_grids: List[Dict] = []
-
-    for pz in page_zones:
-        zone_words = _words_in_zone(
-            all_words, pz.y, pz.height, pz.x, pz.width
-        )
-        if pz.zone_type == "content":
-            logger.info(
-                "build-grid zone %d (%s): bounds x=%d..%d y=%d..%d -> %d/%d words",
-                pz.index, pz.zone_type,
-                pz.x, pz.x + pz.width, pz.y, pz.y + pz.height,
-                len(zone_words), len(all_words),
-            )
-        # Filter recovered single-char artifacts in ALL zones
-        before = len(zone_words)
-        zone_words = [
-            w for w in zone_words
-            if not (
-                w.get("recovered")
-                and len(w.get("text", "").strip()) <= 2
-            )
-        ]
-        removed = before - len(zone_words)
-        if removed:
-            logger.info(
-                "build-grid: filtered %d recovered artifacts from %s zone %d",
-                removed, pz.zone_type, pz.index,
-            )
-        # Filter words inside image overlay regions (merged box zones)
-        if pz.image_overlays:
-            before_ov = len(zone_words)
-            zone_words = [
-                w for w in zone_words
-                if not any(
-                    ov["y"] <= w["top"] + w["height"] / 2 <= ov["y"] + ov["height"]
-                    and ov["x"] <= w["left"] + w["width"] / 2 <= ov["x"] + ov["width"]
-                    for ov in pz.image_overlays
-                )
-            ]
-            ov_removed = before_ov - len(zone_words)
-            if ov_removed:
-                logger.info(
-                    "build-grid: filtered %d words inside image overlays from zone %d",
-                    ov_removed, pz.index,
-                )
-        zone_words, bs_removed = _filter_border_strip_words(zone_words)
-        bp = False
-        if bs_removed:
-            bp = True
-            logger.info(
-                "build-grid: pre-filtered %d border-strip words from zone %d",
-                bs_removed, pz.index,
-            )
-        grid = _build_zone_grid(
-            zone_words, pz.x, pz.y, pz.width, pz.height,
-            pz.index, img_w, img_h,
-            skip_first_row_header=bool(pz.image_overlays),
-        )
-        zone_grids.append({
-            "pz": pz, "words": zone_words, "grid": grid,
-            "_border_prefiltered": bp,
-        })
-
-    return zone_grids
-
-
-def _merge_content_zone_columns(
-    zone_grids: List[Dict[str, Any]],
-    all_words: List[Dict[str, Any]],
-    content_w: int,
-    img_w: int,
-    img_h: int,
-    session_id: str,
-) -> None:
-    """Second pass: merge column boundaries from all content zones.
-
-    Modifies zone_grids in place.
-    """
-    content_zones = [
-        zg for zg in zone_grids
-        if zg["pz"].zone_type == "content"
-        and zg["pz"].vsplit_group is None
-    ]
-    if len(content_zones) <= 1:
-        return
-
-    # Collect column split points (x_min of non-first columns)
-    all_split_xs: List[float] = []
-    for zg in content_zones:
-        raw_cols = zg["grid"].get("_raw_columns", [])
-        for col in raw_cols[1:]:
-            all_split_xs.append(col["x_min"])
-
-    if not all_split_xs:
-        return
-
-    all_split_xs.sort()
-    merge_distance = max(25, int(content_w * 0.03))
-    merged_xs = [all_split_xs[0]]
-    for x in all_split_xs[1:]:
-        if x - merged_xs[-1] < merge_distance:
-            merged_xs[-1] = (merged_xs[-1] + x) / 2
-        else:
-            merged_xs.append(x)
-
-    total_cols = len(merged_xs) + 1
-    max_zone_cols = max(
-        len(zg["grid"].get("_raw_columns", []))
-        for zg in content_zones
-    )
-
-    if total_cols < max_zone_cols:
-        return
-
-    cx_min = min(w["left"] for w in all_words)
-    cx_max = max(w["left"] + w["width"] for w in all_words)
-    merged_columns: List[Dict[str, Any]] = []
-    prev_x = cx_min
-    for i, sx in enumerate(merged_xs):
-        merged_columns.append({
-            "index": i,
-            "type": f"column_{i + 1}",
-            "x_min": prev_x,
-            "x_max": sx,
-        })
-        prev_x = sx
-    merged_columns.append({
-        "index": len(merged_xs),
-        "type": f"column_{len(merged_xs) + 1}",
-        "x_min": prev_x,
-        "x_max": cx_max,
-    })
-
-    # Re-build ALL content zones with merged columns
-    for zg in zone_grids:
-        pz = zg["pz"]
-        if pz.zone_type == "content":
-            grid = _build_zone_grid(
-                zg["words"], pz.x, pz.y,
-                pz.width, pz.height,
-                pz.index, img_w, img_h,
-                global_columns=merged_columns,
-                skip_first_row_header=bool(pz.image_overlays),
-            )
-            zg["grid"] = grid
-    logger.info(
-        "build-grid session %s: union of %d content "
-        "zones -> %d merged columns (max single zone: %d)",
-        session_id, len(content_zones),
-        total_cols, max_zone_cols,
-    )

klausur-service/backend/grid_editor_api.py

@@ -1,31 +1,4 @@
-"""
+# Backward-compat shim -- module moved to grid/editor/api.py
-Grid Editor API — barrel re-export.
+import importlib as _importlib
-
+import sys as _sys
-The actual endpoints live in:
+_sys.modules[__name__] = _importlib.import_module("grid.editor.api")
-  - grid_editor_api_grid.py   (build-grid, rerun-ocr, save-grid, get-grid)
-  - grid_editor_api_gutter.py (gutter-repair, gutter-repair/apply)
-  - grid_editor_api_box.py    (build-box-grids)
-  - grid_editor_api_unified.py (build-unified-grid, unified-grid)
-
-This module re-exports the combined router and key symbols so that
-existing `from grid_editor_api import router` / `from grid_editor_api import _build_grid_core`
-continue to work unchanged.
-"""
-
-from fastapi import APIRouter
-
-from grid_editor_api_grid import router as _grid_router
-from grid_editor_api_gutter import router as _gutter_router
-from grid_editor_api_box import router as _box_router
-from grid_editor_api_unified import router as _unified_router
-
-# Re-export _build_grid_core so callers that do
-# `from grid_editor_api import _build_grid_core` keep working.
-from grid_build_core import _build_grid_core  # noqa: F401
-
-# Merge all sub-routers into one combined router
-router = APIRouter()
-router.include_router(_grid_router)
-router.include_router(_gutter_router)
-router.include_router(_box_router)
-router.include_router(_unified_router)

klausur-service/backend/grid_editor_api_box.py

@@ -1,177 +1,4 @@
-"""
+# Backward-compat shim -- module moved to grid/editor/api_box.py
-Grid Editor API — box-grid-review endpoints.
+import importlib as _importlib
-"""
+import sys as _sys
-
+_sys.modules[__name__] = _importlib.import_module("grid.editor.api_box")
-import logging
-
-from fastapi import APIRouter, HTTPException, Request
-
-from grid_editor_helpers import _words_in_zone
-from ocr_pipeline_session_store import (
-    get_session_db,
-    update_session_db,
-)
-
-logger = logging.getLogger(__name__)
-
-router = APIRouter(prefix="/api/v1/ocr-pipeline", tags=["grid-editor"])
-
-
-@router.post("/sessions/{session_id}/build-box-grids")
-async def build_box_grids(session_id: str, request: Request):
-    """Rebuild grid structure for all detected boxes with layout-aware detection.
-
-    Uses structure_result.boxes (from Step 7) as the source of box coordinates,
-    and raw_paddle_words as OCR word source. Creates or updates box zones in
-    the grid_editor_result.
-
-    Optional body: { "overrides": { "0": "bullet_list" } }
-    Maps box_index -> forced layout_type.
-    """
-    session = await get_session_db(session_id)
-    if not session:
-        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
-
-    grid_data = session.get("grid_editor_result")
-    if not grid_data:
-        raise HTTPException(status_code=400, detail="No grid data. Run build-grid first.")
-
-    # Get raw OCR words (with top/left/width/height keys)
-    word_result = session.get("word_result") or {}
-    all_words = word_result.get("raw_paddle_words") or word_result.get("raw_tesseract_words") or []
-    if not all_words:
-        raise HTTPException(status_code=400, detail="No raw OCR words available.")
-
-    # Get detected boxes from structure_result
-    structure_result = session.get("structure_result") or {}
-    gt = session.get("ground_truth") or {}
-    if not structure_result:
-        structure_result = gt.get("structure_result") or {}
-    detected_boxes = structure_result.get("boxes") or []
-    if not detected_boxes:
-        return {"session_id": session_id, "box_zones_rebuilt": 0, "spell_fixes": 0, "message": "No boxes detected"}
-
-    # Filter out false-positive boxes in header/footer margins.
-    img_h_for_filter = grid_data.get("image_height", 0) or word_result.get("image_height", 0)
-    if img_h_for_filter > 0:
-        margin_frac = 0.07  # 7% of image height
-        margin_top = img_h_for_filter * margin_frac
-        margin_bottom = img_h_for_filter * (1 - margin_frac)
-        filtered = []
-        for box in detected_boxes:
-            by = box.get("y", 0)
-            bh = box.get("h", 0)
-            box_center_y = by + bh / 2
-            if box_center_y < margin_top or box_center_y > margin_bottom:
-                logger.info("build-box-grids: skipping header/footer box at y=%d h=%d (center=%.0f, margins=%.0f/%.0f)",
-                            by, bh, box_center_y, margin_top, margin_bottom)
-                continue
-            filtered.append(box)
-        detected_boxes = filtered
-
-    body = {}
-    try:
-        body = await request.json()
-    except Exception:
-        pass
-    layout_overrides = body.get("overrides", {})
-
-    from cv_box_layout import build_box_zone_grid
-
-    img_w = grid_data.get("image_width", 0) or word_result.get("image_width", 0)
-    img_h = grid_data.get("image_height", 0) or word_result.get("image_height", 0)
-
-    zones = grid_data.get("zones", [])
-
-    # Find highest existing zone_index
-    max_zone_idx = max((z.get("zone_index", 0) for z in zones), default=-1)
-
-    # Remove old box zones (we'll rebuild them)
-    zones = [z for z in zones if z.get("zone_type") != "box"]
-
-    box_count = 0
-    spell_fixes = 0
-
-    for box_idx, box in enumerate(detected_boxes):
-        bx = box.get("x", 0)
-        by = box.get("y", 0)
-        bw = box.get("w", 0)
-        bh = box.get("h", 0)
-
-        if bw <= 0 or bh <= 0:
-            continue
-
-        # Filter raw OCR words inside this box
-        zone_words = _words_in_zone(all_words, by, bh, bx, bw)
-        if not zone_words:
-            logger.info("Box %d: no words found in bbox (%d,%d,%d,%d)", box_idx, bx, by, bw, bh)
-            continue
-
-        zone_idx = max_zone_idx + 1 + box_idx
-        forced_layout = layout_overrides.get(str(box_idx))
-
-        # Build box grid
-        box_grid = build_box_zone_grid(
-            zone_words, bx, by, bw, bh,
-            zone_idx, img_w, img_h,
-            layout_type=forced_layout,
-        )
-
-        # Apply SmartSpellChecker to all box cells
-        try:
-            from smart_spell import SmartSpellChecker
-            ssc = SmartSpellChecker()
-            for cell in box_grid.get("cells", []):
-                text = cell.get("text", "")
-                if not text:
-                    continue
-                result = ssc.correct_text(text, lang="auto")
-                if result.changed:
-                    cell["text"] = result.corrected
-                    spell_fixes += 1
-        except ImportError:
-            pass
-
-        # Build zone entry
-        zone_entry = {
-            "zone_index": zone_idx,
-            "zone_type": "box",
-            "bbox_px": {"x": bx, "y": by, "w": bw, "h": bh},
-            "bbox_pct": {
-                "x": round(bx / img_w * 100, 2) if img_w else 0,
-                "y": round(by / img_h * 100, 2) if img_h else 0,
-                "w": round(bw / img_w * 100, 2) if img_w else 0,
-                "h": round(bh / img_h * 100, 2) if img_h else 0,
-            },
-            "border": None,
-            "word_count": len(zone_words),
-            "columns": box_grid["columns"],
-            "rows": box_grid["rows"],
-            "cells": box_grid["cells"],
-            "header_rows": box_grid.get("header_rows", []),
-            "box_layout_type": box_grid.get("box_layout_type", "flowing"),
-            "box_grid_reviewed": False,
-            "box_bg_color": box.get("bg_color_name", ""),
-            "box_bg_hex": box.get("bg_color_hex", ""),
-        }
-        zones.append(zone_entry)
-        box_count += 1
-
-    # Sort zones by y-position for correct reading order
-    zones.sort(key=lambda z: z.get("bbox_px", {}).get("y", 0))
-
-    grid_data["zones"] = zones
-    await update_session_db(session_id, grid_editor_result=grid_data)
-
-    logger.info(
-        "build-box-grids session %s: %d boxes processed (%d words spell-fixed) from %d detected",
-        session_id, box_count, spell_fixes, len(detected_boxes),
-    )
-
-    return {
-        "session_id": session_id,
-        "box_zones_rebuilt": box_count,
-        "total_detected_boxes": len(detected_boxes),
-        "spell_fixes": spell_fixes,
-        "zones": zones,
-    }

klausur-service/backend/grid_editor_api_grid.py

@@ -1,337 +1,4 @@
-"""
+# Backward-compat shim -- module moved to grid/editor/api_grid.py
-Grid Editor API — grid build, save, and retrieve endpoints.
+import importlib as _importlib
-"""
+import sys as _sys
-
+_sys.modules[__name__] = _importlib.import_module("grid.editor.api_grid")
-import logging
-import time
-from typing import Any, Dict
-
-from fastapi import APIRouter, HTTPException, Query, Request
-
-from grid_build_core import _build_grid_core
-from ocr_pipeline_session_store import (
-    get_session_db,
-    update_session_db,
-)
-from ocr_pipeline_common import (
-    _cache,
-    _load_session_to_cache,
-    _get_cached,
-)
-
-logger = logging.getLogger(__name__)
-
-router = APIRouter(prefix="/api/v1/ocr-pipeline", tags=["grid-editor"])
-
-
-@router.post("/sessions/{session_id}/build-grid")
-async def build_grid(
-    session_id: str,
-    ipa_mode: str = Query("auto", pattern="^(auto|all|de|en|none)$"),
-    syllable_mode: str = Query("auto", pattern="^(auto|all|de|en|none)$"),
-    enhance: bool = Query(True, description="Step 3: CLAHE + denoise for degraded scans"),
-    max_cols: int = Query(0, description="Step 2: Max column count (0=unlimited)"),
-    min_conf: int = Query(0, description="Step 1: Min OCR confidence (0=auto)"),
-):
-    """Build a structured, zone-aware grid from existing Kombi word results.
-
-    Requires that paddle-kombi or rapid-kombi has already been run on the session.
-    Uses the image for box detection and the word positions for grid structuring.
-
-    Query params:
-        ipa_mode: "auto" (only when English IPA detected), "all" (force), "none" (skip)
-        syllable_mode: "auto" (only when original has dividers), "all" (force), "none" (skip)
-
-    Returns a StructuredGrid with zones, each containing their own
-    columns, rows, and cells — ready for the frontend Excel-like editor.
-    """
-    session = await get_session_db(session_id)
-    if not session:
-        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
-
-    try:
-        result = await _build_grid_core(
-            session_id, session,
-            ipa_mode=ipa_mode, syllable_mode=syllable_mode,
-            enhance=enhance,
-            max_columns=max_cols if max_cols > 0 else None,
-            min_conf=min_conf if min_conf > 0 else None,
-        )
-    except ValueError as e:
-        raise HTTPException(status_code=400, detail=str(e))
-
-    # Save automatic grid snapshot for later comparison with manual corrections
-    # Lazy import to avoid circular dependency with ocr_pipeline_regression
-    from ocr_pipeline_regression import _build_reference_snapshot
-
-    wr = session.get("word_result") or {}
-    engine = wr.get("ocr_engine", "")
-    if engine in ("kombi", "rapid_kombi"):
-        auto_pipeline = "kombi"
-    elif engine == "paddle_direct":
-        auto_pipeline = "paddle-direct"
-    else:
-        auto_pipeline = "pipeline"
-    auto_snapshot = _build_reference_snapshot(result, pipeline=auto_pipeline)
-
-    gt = session.get("ground_truth") or {}
-    gt["auto_grid_snapshot"] = auto_snapshot
-
-    # Persist to DB and advance current_step to 11 (reconstruction complete)
-    await update_session_db(session_id, grid_editor_result=result, ground_truth=gt, current_step=11)
-
-    logger.info(
-        "build-grid session %s: %d zones, %d cols, %d rows, %d cells, "
-        "%d boxes in %.2fs",
-        session_id,
-        len(result.get("zones", [])),
-        result.get("summary", {}).get("total_columns", 0),
-        result.get("summary", {}).get("total_rows", 0),
-        result.get("summary", {}).get("total_cells", 0),
-        result.get("boxes_detected", 0),
-        result.get("duration_seconds", 0),
-    )
-
-    return result
-
-
-@router.post("/sessions/{session_id}/rerun-ocr-and-build-grid")
-async def rerun_ocr_and_build_grid(
-    session_id: str,
-    ipa_mode: str = Query("auto", pattern="^(auto|all|de|en|none)$"),
-    syllable_mode: str = Query("auto", pattern="^(auto|all|de|en|none)$"),
-    enhance: bool = Query(True, description="Step 3: CLAHE + denoise for degraded scans"),
-    max_cols: int = Query(0, description="Step 2: Max column count (0=unlimited)"),
-    min_conf: int = Query(0, description="Step 1: Min OCR confidence (0=auto)"),
-    vision_fusion: bool = Query(False, description="Step 4: Vision-LLM fusion for degraded scans"),
-    doc_category: str = Query("", description="Document type for Vision-LLM prompt context"),
-):
-    """Re-run OCR with quality settings, then rebuild the grid.
-
-    Unlike build-grid (which only rebuilds from existing words),
-    this endpoint re-runs the full OCR pipeline on the cropped image
-    with optional CLAHE enhancement, then builds the grid.
-
-    Steps executed: Image Enhancement -> OCR -> Grid Build
-    """
-    session = await get_session_db(session_id)
-    if not session:
-        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
-
-    import time as _time
-    t0 = _time.time()
-
-    # 1. Load the cropped/dewarped image from cache or session
-    if session_id not in _cache:
-        await _load_session_to_cache(session_id)
-    cached = _get_cached(session_id)
-
-    dewarped_bgr = cached.get("cropped_bgr") if cached.get("cropped_bgr") is not None else cached.get("dewarped_bgr")
-    if dewarped_bgr is None:
-        raise HTTPException(status_code=400, detail="No cropped/dewarped image available. Run preprocessing steps first.")
-
-    import numpy as np
-    img_h, img_w = dewarped_bgr.shape[:2]
-    ocr_input = dewarped_bgr.copy()
-
-    # 2. Scan quality assessment
-    scan_quality_info = {}
-    try:
-        from scan_quality import score_scan_quality
-        quality_report = score_scan_quality(ocr_input)
-        scan_quality_info = quality_report.to_dict()
-        actual_min_conf = min_conf if min_conf > 0 else quality_report.recommended_min_conf
-    except Exception as e:
-        logger.warning(f"rerun-ocr: scan quality failed: {e}")
-        actual_min_conf = min_conf if min_conf > 0 else 40
-
-    # 3. Image enhancement (Step 3)
-    is_degraded = scan_quality_info.get("is_degraded", False)
-    if enhance and is_degraded:
-        try:
-            from ocr_image_enhance import enhance_for_ocr
-            ocr_input = enhance_for_ocr(ocr_input, is_degraded=True)
-            logger.info("rerun-ocr: CLAHE enhancement applied")
-        except Exception as e:
-            logger.warning(f"rerun-ocr: enhancement failed: {e}")
-
-    # 4. Run dual-engine OCR
-    from PIL import Image
-    import pytesseract
-
-    # RapidOCR
-    rapid_words = []
-    try:
-        from cv_ocr_engines import ocr_region_rapid
-        from cv_vocab_types import PageRegion
-        full_region = PageRegion(type="full_page", x=0, y=0, width=img_w, height=img_h)
-        rapid_words = ocr_region_rapid(ocr_input, full_region) or []
-    except Exception as e:
-        logger.warning(f"rerun-ocr: RapidOCR failed: {e}")
-
-    # Tesseract
-    pil_img = Image.fromarray(ocr_input[:, :, ::-1])
-    data = pytesseract.image_to_data(pil_img, lang='eng+deu', config='--psm 6 --oem 3', output_type=pytesseract.Output.DICT)
-    tess_words = []
-    for i in range(len(data["text"])):
-        text = (data["text"][i] or "").strip()
-        conf_raw = str(data["conf"][i])
-        conf = int(conf_raw) if conf_raw.lstrip("-").isdigit() else -1
-        if not text or conf < actual_min_conf:
-            continue
-        tess_words.append({
-            "text": text, "left": data["left"][i], "top": data["top"][i],
-            "width": data["width"][i], "height": data["height"][i], "conf": conf,
-        })
-
-    # 5. Merge OCR results
-    from ocr_pipeline_ocr_merge import _split_paddle_multi_words, _merge_paddle_tesseract, _deduplicate_words
-    rapid_split = _split_paddle_multi_words(rapid_words) if rapid_words else []
-    if rapid_split or tess_words:
-        merged_words = _merge_paddle_tesseract(rapid_split, tess_words)
-        merged_words = _deduplicate_words(merged_words)
-    else:
-        merged_words = tess_words
-
-    # 6. Store updated word_result in session
-    cells_for_storage = [{"text": w["text"], "left": w["left"], "top": w["top"],
-                          "width": w["width"], "height": w["height"], "conf": w.get("conf", 0)}
-                         for w in merged_words]
-    word_result = {
-        "cells": [{"text": " ".join(w["text"] for w in merged_words),
-                    "word_boxes": cells_for_storage}],
-        "image_width": img_w,
-        "image_height": img_h,
-        "ocr_engine": "rapid_kombi",
-        "word_count": len(merged_words),
-        "raw_paddle_words": rapid_words,
-    }
-    # 6b. Vision-LLM Fusion (Step 4) — correct OCR using Vision model
-    vision_applied = False
-    if vision_fusion:
-        try:
-            from vision_ocr_fusion import vision_fuse_ocr
-            category = doc_category or session.get("document_category") or "vokabelseite"
-            logger.info(f"rerun-ocr: running Vision-LLM fusion (category={category})")
-            merged_words = await vision_fuse_ocr(ocr_input, merged_words, category)
-            vision_applied = True
-            # Rebuild storage from fused words
-            cells_for_storage = [{"text": w["text"], "left": w["left"], "top": w["top"],
-                                  "width": w["width"], "height": w["height"], "conf": w.get("conf", 0)}
-                                 for w in merged_words]
-            word_result["cells"] = [{"text": " ".join(w["text"] for w in merged_words),
-                                     "word_boxes": cells_for_storage}]
-            word_result["word_count"] = len(merged_words)
-            word_result["ocr_engine"] = "vision_fusion"
-        except Exception as e:
-            logger.warning(f"rerun-ocr: Vision-LLM fusion failed: {e}")
-
-    await update_session_db(session_id, word_result=word_result)
-
-    # Reload session with updated word_result
-    session = await get_session_db(session_id)
-
-    ocr_duration = _time.time() - t0
-    logger.info(
-        "rerun-ocr session %s: %d words (rapid=%d, tess=%d, merged=%d) in %.1fs "
-        "(enhance=%s, min_conf=%d, quality=%s)",
-        session_id, len(merged_words), len(rapid_words), len(tess_words),
-        len(merged_words), ocr_duration, enhance, actual_min_conf,
-        scan_quality_info.get("quality_pct", "?"),
-    )
-
-    # 7. Build grid from new words
-    try:
-        result = await _build_grid_core(
-            session_id, session,
-            ipa_mode=ipa_mode, syllable_mode=syllable_mode,
-            enhance=enhance,
-            max_columns=max_cols if max_cols > 0 else None,
-            min_conf=min_conf if min_conf > 0 else None,
-        )
-    except ValueError as e:
-        raise HTTPException(status_code=400, detail=str(e))
-
-    # Persist grid
-    await update_session_db(session_id, grid_editor_result=result, current_step=11)
-
-    # Add quality info to response
-    result["scan_quality"] = scan_quality_info
-    result["ocr_stats"] = {
-        "rapid_words": len(rapid_words),
-        "tess_words": len(tess_words),
-        "merged_words": len(merged_words),
-        "min_conf_used": actual_min_conf,
-        "enhance_applied": enhance and is_degraded,
-        "vision_fusion_applied": vision_applied,
-        "document_category": doc_category or session.get("document_category", ""),
-        "ocr_duration_seconds": round(ocr_duration, 1),
-    }
-
-    total_duration = _time.time() - t0
-    logger.info(
-        "rerun-ocr+build-grid session %s: %d zones, %d cols, %d cells in %.1fs",
-        session_id,
-        len(result.get("zones", [])),
-        result.get("summary", {}).get("total_columns", 0),
-        result.get("summary", {}).get("total_cells", 0),
-        total_duration,
-    )
-
-    return result
-
-
-@router.post("/sessions/{session_id}/save-grid")
-async def save_grid(session_id: str, request: Request):
-    """Save edited grid data from the frontend Excel-like editor.
-
-    Receives the full StructuredGrid with user edits (text changes,
-    formatting changes like bold columns, header rows, etc.) and
-    persists it to the session's grid_editor_result.
-    """
-    session = await get_session_db(session_id)
-    if not session:
-        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
-
-    body = await request.json()
-
-    # Validate basic structure
-    if "zones" not in body:
-        raise HTTPException(status_code=400, detail="Missing 'zones' in request body")
-
-    # Preserve metadata from the original build
-    existing = session.get("grid_editor_result") or {}
-    result = {
-        "session_id": session_id,
-        "image_width": body.get("image_width", existing.get("image_width", 0)),
-        "image_height": body.get("image_height", existing.get("image_height", 0)),
-        "zones": body["zones"],
-        "boxes_detected": body.get("boxes_detected", existing.get("boxes_detected", 0)),
-        "summary": body.get("summary", existing.get("summary", {})),
-        "formatting": body.get("formatting", existing.get("formatting", {})),
-        "duration_seconds": existing.get("duration_seconds", 0),
-        "edited": True,
-    }
-
-    await update_session_db(session_id, grid_editor_result=result, current_step=11)
-
-    logger.info("save-grid session %s: %d zones saved", session_id, len(body["zones"]))
-
-    return {"session_id": session_id, "saved": True}
-
-
-@router.get("/sessions/{session_id}/grid-editor")
-async def get_grid(session_id: str):
-    """Retrieve the current grid editor state for a session."""
-    session = await get_session_db(session_id)
-    if not session:
-        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
-
-    result = session.get("grid_editor_result")
-    if not result:
-        raise HTTPException(
-            status_code=404,
-            detail="No grid editor data. Run build-grid first.",
-        )
-
-    return result

klausur-service/backend/grid_editor_api_gutter.py

@@ -1,110 +1,4 @@
-"""
+# Backward-compat shim -- module moved to grid/editor/api_gutter.py
-Grid Editor API — gutter repair endpoints.
+import importlib as _importlib
-"""
+import sys as _sys
-
+_sys.modules[__name__] = _importlib.import_module("grid.editor.api_gutter")
-import logging
-
-from fastapi import APIRouter, HTTPException, Request
-
-from ocr_pipeline_session_store import (
-    get_session_db,
-    update_session_db,
-)
-
-logger = logging.getLogger(__name__)
-
-router = APIRouter(prefix="/api/v1/ocr-pipeline", tags=["grid-editor"])
-
-
-@router.post("/sessions/{session_id}/gutter-repair")
-async def gutter_repair(session_id: str):
-    """Analyse grid for gutter-edge OCR errors and return repair suggestions.
-
-    Detects:
-      - Words truncated/blurred at the book binding (spell_fix)
-      - Words split across rows with missing hyphen chars (hyphen_join)
-    """
-    session = await get_session_db(session_id)
-    if not session:
-        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
-
-    grid_data = session.get("grid_editor_result")
-    if not grid_data:
-        raise HTTPException(
-            status_code=400,
-            detail="No grid data. Run build-grid first.",
-        )
-
-    from cv_gutter_repair import analyse_grid_for_gutter_repair
-
-    image_width = grid_data.get("image_width", 0)
-    result = analyse_grid_for_gutter_repair(grid_data, image_width=image_width)
-
-    # Persist suggestions in ground_truth.gutter_repair (avoids DB migration)
-    gt = session.get("ground_truth") or {}
-    gt["gutter_repair"] = result
-    await update_session_db(session_id, ground_truth=gt)
-
-    logger.info(
-        "gutter-repair session %s: %d suggestions in %.2fs",
-        session_id,
-        result.get("stats", {}).get("suggestions_found", 0),
-        result.get("duration_seconds", 0),
-    )
-
-    return result
-
-
-@router.post("/sessions/{session_id}/gutter-repair/apply")
-async def gutter_repair_apply(session_id: str, request: Request):
-    """Apply accepted gutter repair suggestions to the grid.
-
-    Body: { "accepted": ["suggestion_id_1", "suggestion_id_2", ...] }
-    """
-    session = await get_session_db(session_id)
-    if not session:
-        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
-
-    grid_data = session.get("grid_editor_result")
-    if not grid_data:
-        raise HTTPException(status_code=400, detail="No grid data.")
-
-    gt = session.get("ground_truth") or {}
-    gutter_result = gt.get("gutter_repair")
-    if not gutter_result:
-        raise HTTPException(
-            status_code=400,
-            detail="No gutter repair data. Run gutter-repair first.",
-        )
-
-    body = await request.json()
-    accepted_ids = body.get("accepted", [])
-    if not accepted_ids:
-        return {"applied_count": 0, "changes": []}
-
-    # text_overrides: { suggestion_id: "alternative_text" }
-    # Allows the user to pick a different correction from the alternatives list
-    text_overrides = body.get("text_overrides", {})
-
-    from cv_gutter_repair import apply_gutter_suggestions
-
-    suggestions = gutter_result.get("suggestions", [])
-
-    # Apply user-selected alternatives before passing to apply
-    for s in suggestions:
-        sid = s.get("id", "")
-        if sid in text_overrides and text_overrides[sid]:
-            s["suggested_text"] = text_overrides[sid]
-
-    result = apply_gutter_suggestions(grid_data, accepted_ids, suggestions)
-
-    # Save updated grid back to session
-    await update_session_db(session_id, grid_editor_result=grid_data)
-
-    logger.info(
-        "gutter-repair/apply session %s: %d changes applied",
-        session_id,
-        result.get("applied_count", 0),
-    )
-
-    return result

klausur-service/backend/grid_editor_api_unified.py

@@ -1,71 +1,4 @@
-"""
+# Backward-compat shim -- module moved to grid/editor/api_unified.py
-Grid Editor API — unified grid endpoints.
+import importlib as _importlib
-"""
+import sys as _sys
-
+_sys.modules[__name__] = _importlib.import_module("grid.editor.api_unified")
-import logging
-
-from fastapi import APIRouter, HTTPException
-
-from ocr_pipeline_session_store import (
-    get_session_db,
-    update_session_db,
-)
-
-logger = logging.getLogger(__name__)
-
-router = APIRouter(prefix="/api/v1/ocr-pipeline", tags=["grid-editor"])
-
-
-@router.post("/sessions/{session_id}/build-unified-grid")
-async def build_unified_grid_endpoint(session_id: str):
-    """Build a single-zone unified grid merging content + box zones.
-
-    Takes the existing multi-zone grid_editor_result and produces a
-    unified grid where boxes are integrated into the main row sequence.
-    Persists as unified_grid_result (preserves original multi-zone data).
-    """
-    session = await get_session_db(session_id)
-    if not session:
-        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
-
-    grid_data = session.get("grid_editor_result")
-    if not grid_data:
-        raise HTTPException(status_code=400, detail="No grid data. Run build-grid first.")
-
-    from unified_grid import build_unified_grid
-
-    result = build_unified_grid(
-        zones=grid_data.get("zones", []),
-        image_width=grid_data.get("image_width", 0),
-        image_height=grid_data.get("image_height", 0),
-        layout_metrics=grid_data.get("layout_metrics", {}),
-    )
-
-    # Persist as separate field (don't overwrite original multi-zone grid)
-    await update_session_db(session_id, unified_grid_result=result)
-
-    logger.info(
-        "build-unified-grid session %s: %d rows, %d cells",
-        session_id,
-        result.get("summary", {}).get("total_rows", 0),
-        result.get("summary", {}).get("total_cells", 0),
-    )
-
-    return result
-
-
-@router.get("/sessions/{session_id}/unified-grid")
-async def get_unified_grid(session_id: str):
-    """Retrieve the unified grid for a session."""
-    session = await get_session_db(session_id)
-    if not session:
-        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
-
-    result = session.get("unified_grid_result")
-    if not result:
-        raise HTTPException(
-            status_code=404,
-            detail="No unified grid. Run build-unified-grid first.",
-        )
-
-    return result

klausur-service/backend/grid_editor_columns.py

@@ -1,492 +1,4 @@
-"""
+# Backward-compat shim -- module moved to grid/editor/columns.py
-Grid Editor — column detection, cross-column splitting, marker merging.
+import importlib as _importlib
-
+import sys as _sys
-Split from grid_editor_helpers.py for maintainability.
+_sys.modules[__name__] = _importlib.import_module("grid.editor.columns")
-All functions are pure computation — no HTTP, DB, or session side effects.
-
-Lizenz: Apache 2.0 (kommerziell nutzbar)
-DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
-"""
-
-import logging
-import re
-from typing import Any, Dict, List, Optional
-
-logger = logging.getLogger(__name__)
-
-
-# ---------------------------------------------------------------------------
-# Cross-column word splitting
-# ---------------------------------------------------------------------------
-
-_spell_cache: Optional[Any] = None
-_spell_loaded = False
-
-
-def _is_recognized_word(text: str) -> bool:
-    """Check if *text* is a recognized German or English word.
-
-    Uses the spellchecker library (same as cv_syllable_detect.py).
-    Returns True for real words like "oder", "Kabel", "Zeitung".
-    Returns False for OCR merge artifacts like "sichzie", "dasZimmer".
-    """
-    global _spell_cache, _spell_loaded
-    if not text or len(text) < 2:
-        return False
-
-    if not _spell_loaded:
-        _spell_loaded = True
-        try:
-            from spellchecker import SpellChecker
-            _spell_cache = SpellChecker(language="de")
-        except Exception:
-            pass
-
-    if _spell_cache is None:
-        return False
-
-    return text.lower() in _spell_cache
-
-
-def _split_cross_column_words(
-    words: List[Dict],
-    columns: List[Dict],
-) -> List[Dict]:
-    """Split word boxes that span across column boundaries.
-
-    When OCR merges adjacent words from different columns (e.g. "sichzie"
-    spanning Col 1 and Col 2, or "dasZimmer" crossing the boundary),
-    split the word box at the column boundary so each piece is assigned
-    to the correct column.
-
-    Only splits when:
-    - The word has significant overlap (>15% of its width) on both sides
-    - AND the word is not a recognized real word (OCR merge artifact), OR
-      the word contains a case transition (lowercase->uppercase) near the
-      boundary indicating two merged words like "dasZimmer".
-    """
-    if len(columns) < 2:
-        return words
-
-    # Column boundaries = midpoints between adjacent column edges
-    boundaries = []
-    for i in range(len(columns) - 1):
-        boundary = (columns[i]["x_max"] + columns[i + 1]["x_min"]) / 2
-        boundaries.append(boundary)
-
-    new_words: List[Dict] = []
-    split_count = 0
-    for w in words:
-        w_left = w["left"]
-        w_width = w["width"]
-        w_right = w_left + w_width
-        text = (w.get("text") or "").strip()
-
-        if not text or len(text) < 4 or w_width < 10:
-            new_words.append(w)
-            continue
-
-        # Find the first boundary this word straddles significantly
-        split_boundary = None
-        for b in boundaries:
-            if w_left < b < w_right:
-                left_part = b - w_left
-                right_part = w_right - b
-                # Both sides must have at least 15% of the word width
-                if left_part > w_width * 0.15 and right_part > w_width * 0.15:
-                    split_boundary = b
-                    break
-
-        if split_boundary is None:
-            new_words.append(w)
-            continue
-
-        # Compute approximate split position in the text.
-        left_width = split_boundary - w_left
-        split_ratio = left_width / w_width
-        approx_pos = len(text) * split_ratio
-
-        # Strategy 1: look for a case transition (lowercase->uppercase) near
-        # the approximate split point — e.g. "dasZimmer" splits at 'Z'.
-        split_char = None
-        search_lo = max(1, int(approx_pos) - 3)
-        search_hi = min(len(text), int(approx_pos) + 2)
-        for i in range(search_lo, search_hi):
-            if text[i - 1].islower() and text[i].isupper():
-                split_char = i
-                break
-
-        # Strategy 2: if no case transition, only split if the whole word
-        # is NOT a real word (i.e. it's an OCR merge artifact like "sichzie").
-        # Real words like "oder", "Kabel", "Zeitung" must not be split.
-        if split_char is None:
-            clean = re.sub(r"[,;:.!?]+$", "", text)  # strip trailing punct
-            if _is_recognized_word(clean):
-                new_words.append(w)
-                continue
-            # Not a real word — use floor of proportional position
-            split_char = max(1, min(len(text) - 1, int(approx_pos)))
-
-        left_text = text[:split_char].rstrip()
-        right_text = text[split_char:].lstrip()
-
-        if len(left_text) < 2 or len(right_text) < 2:
-            new_words.append(w)
-            continue
-
-        right_width = w_width - round(left_width)
-        new_words.append({
-            **w,
-            "text": left_text,
-            "width": round(left_width),
-        })
-        new_words.append({
-            **w,
-            "text": right_text,
-            "left": round(split_boundary),
-            "width": right_width,
-        })
-        split_count += 1
-        logger.info(
-            "split cross-column word %r -> %r + %r at boundary %.0f",
-            text, left_text, right_text, split_boundary,
-        )
-
-    if split_count:
-        logger.info("split %d cross-column word(s)", split_count)
-    return new_words
-
-
-def _cluster_columns_by_alignment(
-    words: List[Dict],
-    zone_w: int,
-    rows: List[Dict],
-) -> List[Dict[str, Any]]:
-    """Detect columns by clustering left-edge alignment across rows.
-
-    Hybrid approach:
-      1. Group words by row, find "group start" positions within each row
-         (words preceded by a large gap or first word in row)
-      2. Cluster group-start left-edges by X-proximity across rows
-      3. Filter by row coverage (how many rows have a group start here)
-      4. Merge nearby clusters
-      5. Build column boundaries
-
-    This filters out mid-phrase word positions (e.g. IPA transcriptions,
-    second words in multi-word entries) by only considering positions
-    where a new word group begins within a row.
-    """
-    if not words or not rows:
-        return []
-
-    total_rows = len(rows)
-    if total_rows == 0:
-        return []
-
-    # --- Group words by row ---
-    row_words: Dict[int, List[Dict]] = {}
-    for w in words:
-        y_center = w["top"] + w["height"] / 2
-        best = min(rows, key=lambda r: abs(r["y_center"] - y_center))
-        row_words.setdefault(best["index"], []).append(w)
-
-    # --- Compute adaptive gap threshold for group-start detection ---
-    all_gaps: List[float] = []
-    for ri, rw_list in row_words.items():
-        sorted_rw = sorted(rw_list, key=lambda w: w["left"])
-        for i in range(len(sorted_rw) - 1):
-            right = sorted_rw[i]["left"] + sorted_rw[i]["width"]
-            gap = sorted_rw[i + 1]["left"] - right
-            if gap > 0:
-                all_gaps.append(gap)
-
-    if all_gaps:
-        sorted_gaps = sorted(all_gaps)
-        median_gap = sorted_gaps[len(sorted_gaps) // 2]
-        heights = [w["height"] for w in words if w.get("height", 0) > 0]
-        median_h = sorted(heights)[len(heights) // 2] if heights else 25
-
-        # For small word counts (boxes, sub-zones): PaddleOCR returns
-        # multi-word blocks, so ALL inter-word gaps are potential column
-        # boundaries.  Use a low threshold based on word height — any gap
-        # wider than ~1x median word height is a column separator.
-        if len(words) <= 60:
-            gap_threshold = max(median_h * 1.0, 25)
-            logger.info(
-                "alignment columns (small zone): gap_threshold=%.0f "
-                "(median_h=%.0f, %d words, %d gaps: %s)",
-                gap_threshold, median_h, len(words), len(sorted_gaps),
-                [int(g) for g in sorted_gaps[:10]],
-            )
-        else:
-            # Standard approach for large zones (full pages)
-            gap_threshold = max(median_gap * 3, median_h * 1.5, 30)
-            # Cap at 25% of zone width
-            max_gap = zone_w * 0.25
-            if gap_threshold > max_gap > 30:
-                logger.info("alignment columns: capping gap_threshold %.0f -> %.0f (25%% of zone_w=%d)", gap_threshold, max_gap, zone_w)
-                gap_threshold = max_gap
-    else:
-        gap_threshold = 50
-
-    # --- Find group-start positions (left-edges that begin a new column) ---
-    start_positions: List[tuple] = []  # (left_edge, row_index)
-    for ri, rw_list in row_words.items():
-        sorted_rw = sorted(rw_list, key=lambda w: w["left"])
-        # First word in row is always a group start
-        start_positions.append((sorted_rw[0]["left"], ri))
-        for i in range(1, len(sorted_rw)):
-            right_prev = sorted_rw[i - 1]["left"] + sorted_rw[i - 1]["width"]
-            gap = sorted_rw[i]["left"] - right_prev
-            if gap >= gap_threshold:
-                start_positions.append((sorted_rw[i]["left"], ri))
-
-    start_positions.sort(key=lambda x: x[0])
-
-    logger.info(
-        "alignment columns: %d group-start positions from %d words "
-        "(gap_threshold=%.0f, %d rows)",
-        len(start_positions), len(words), gap_threshold, total_rows,
-    )
-
-    if not start_positions:
-        x_min = min(w["left"] for w in words)
-        x_max = max(w["left"] + w["width"] for w in words)
-        return [{"index": 0, "type": "column_text", "x_min": x_min, "x_max": x_max}]
-
-    # --- Cluster group-start positions by X-proximity ---
-    tolerance = max(10, int(zone_w * 0.01))
-    clusters: List[Dict[str, Any]] = []
-    cur_edges = [start_positions[0][0]]
-    cur_rows = {start_positions[0][1]}
-
-    for left, row_idx in start_positions[1:]:
-        if left - cur_edges[-1] <= tolerance:
-            cur_edges.append(left)
-            cur_rows.add(row_idx)
-        else:
-            clusters.append({
-                "mean_x": int(sum(cur_edges) / len(cur_edges)),
-                "min_edge": min(cur_edges),
-                "max_edge": max(cur_edges),
-                "count": len(cur_edges),
-                "distinct_rows": len(cur_rows),
-                "row_coverage": len(cur_rows) / total_rows,
-            })
-            cur_edges = [left]
-            cur_rows = {row_idx}
-    clusters.append({
-        "mean_x": int(sum(cur_edges) / len(cur_edges)),
-        "min_edge": min(cur_edges),
-        "max_edge": max(cur_edges),
-        "count": len(cur_edges),
-        "distinct_rows": len(cur_rows),
-        "row_coverage": len(cur_rows) / total_rows,
-    })
-
-    # --- Filter by row coverage ---
-    # These thresholds must be high enough to avoid false columns in flowing
-    # text (random inter-word gaps) while still detecting real columns in
-    # vocabulary worksheets (which typically have >80% row coverage).
-    MIN_COVERAGE_PRIMARY = 0.35
-    MIN_COVERAGE_SECONDARY = 0.12
-    MIN_WORDS_SECONDARY = 4
-    MIN_DISTINCT_ROWS = 3
-
-    # Content boundary for left-margin detection
-    content_x_min = min(w["left"] for w in words)
-    content_x_max = max(w["left"] + w["width"] for w in words)
-    content_span = content_x_max - content_x_min
-
-    primary = [
-        c for c in clusters
-        if c["row_coverage"] >= MIN_COVERAGE_PRIMARY
-        and c["distinct_rows"] >= MIN_DISTINCT_ROWS
-    ]
-    primary_ids = {id(c) for c in primary}
-    secondary = [
-        c for c in clusters
-        if id(c) not in primary_ids
-        and c["row_coverage"] >= MIN_COVERAGE_SECONDARY
-        and c["count"] >= MIN_WORDS_SECONDARY
-        and c["distinct_rows"] >= MIN_DISTINCT_ROWS
-    ]
-
-    # Tertiary: narrow left-margin columns (page refs, markers) that have
-    # too few rows for secondary but are clearly left-aligned and separated
-    # from the main content.  These appear at the far left or far right and
-    # have a large gap to the nearest significant cluster.
-    used_ids = {id(c) for c in primary} | {id(c) for c in secondary}
-    sig_xs = [c["mean_x"] for c in primary + secondary]
-
-    # Tertiary: clusters that are clearly to the LEFT of the first
-    # significant column (or RIGHT of the last).  If words consistently
-    # start at a position left of the established first column boundary,
-    # they MUST be a separate column — regardless of how few rows they
-    # cover.  The only requirement is a clear spatial gap.
-    MIN_COVERAGE_TERTIARY = 0.02  # at least 1 row effectively
-    tertiary = []
-    for c in clusters:
-        if id(c) in used_ids:
-            continue
-        if c["distinct_rows"] < 1:
-            continue
-        if c["row_coverage"] < MIN_COVERAGE_TERTIARY:
-            continue
-        # Must be near left or right content margin (within 15%)
-        rel_pos = (c["mean_x"] - content_x_min) / content_span if content_span else 0.5
-        if not (rel_pos < 0.15 or rel_pos > 0.85):
-            continue
-        # Must have significant gap to nearest significant cluster
-        if sig_xs:
-            min_dist = min(abs(c["mean_x"] - sx) for sx in sig_xs)
-            if min_dist < max(30, content_span * 0.02):
-                continue
-        tertiary.append(c)
-
-    if tertiary:
-        for c in tertiary:
-            logger.info(
-                "  tertiary (margin) cluster: x=%d (range %d-%d), %d words, %d rows (%.0f%%)",
-                c["mean_x"], c["min_edge"], c["max_edge"],
-                c["count"], c["distinct_rows"], c["row_coverage"] * 100,
-            )
-
-    significant = sorted(primary + secondary + tertiary, key=lambda c: c["mean_x"])
-
-    for c in significant:
-        logger.info(
-            "  significant cluster: x=%d (range %d-%d), %d words, %d rows (%.0f%%)",
-            c["mean_x"], c["min_edge"], c["max_edge"],
-            c["count"], c["distinct_rows"], c["row_coverage"] * 100,
-        )
-    logger.info(
-        "alignment columns: %d clusters, %d primary, %d secondary -> %d significant",
-        len(clusters), len(primary), len(secondary), len(significant),
-    )
-
-    if not significant:
-        # Fallback: single column covering all content
-        x_min = min(w["left"] for w in words)
-        x_max = max(w["left"] + w["width"] for w in words)
-        return [{"index": 0, "type": "column_text", "x_min": x_min, "x_max": x_max}]
-
-    # --- Merge nearby clusters ---
-    merge_distance = max(25, int(zone_w * 0.03))
-    merged = [significant[0].copy()]
-    for s in significant[1:]:
-        if s["mean_x"] - merged[-1]["mean_x"] < merge_distance:
-            prev = merged[-1]
-            total = prev["count"] + s["count"]
-            prev["mean_x"] = (
-                prev["mean_x"] * prev["count"] + s["mean_x"] * s["count"]
-            ) // total
-            prev["count"] = total
-            prev["min_edge"] = min(prev["min_edge"], s["min_edge"])
-            prev["max_edge"] = max(prev["max_edge"], s["max_edge"])
-            prev["distinct_rows"] = max(prev["distinct_rows"], s["distinct_rows"])
-        else:
-            merged.append(s.copy())
-
-    logger.info(
-        "alignment columns: %d after merge (distance=%d)",
-        len(merged), merge_distance,
-    )
-
-    # --- Build column boundaries ---
-    margin = max(5, int(zone_w * 0.005))
-    content_x_min = min(w["left"] for w in words)
-    content_x_max = max(w["left"] + w["width"] for w in words)
-
-    columns: List[Dict[str, Any]] = []
-    for i, cluster in enumerate(merged):
-        x_min = max(content_x_min, cluster["min_edge"] - margin)
-        if i + 1 < len(merged):
-            x_max = merged[i + 1]["min_edge"] - margin
-        else:
-            x_max = content_x_max
-
-        columns.append({
-            "index": i,
-            "type": f"column_{i + 1}" if len(merged) > 1 else "column_text",
-            "x_min": x_min,
-            "x_max": x_max,
-        })
-
-    return columns
-
-
-_MARKER_CHARS = set("*-+#>")
-
-
-def _merge_inline_marker_columns(
-    columns: List[Dict],
-    words: List[Dict],
-) -> List[Dict]:
-    """Merge narrow marker columns (bullets, numbering) into adjacent text.
-
-    Bullet points (*, -) and numbering (1., 2.) create narrow columns
-    at the left edge of a zone.  These are inline markers that indent text,
-    not real separate columns.  Merge them with their right neighbour.
-
-    Does NOT merge columns containing alphabetic words like "to", "in",
-    "der", "die", "das" — those are legitimate content columns.
-    """
-    if len(columns) < 2:
-        return columns
-
-    merged: List[Dict] = []
-    skip: set = set()
-
-    for i, col in enumerate(columns):
-        if i in skip:
-            continue
-
-        # Find words in this column
-        col_words = [
-            w for w in words
-            if col["x_min"] <= w["left"] + w["width"] / 2 < col["x_max"]
-        ]
-        col_width = col["x_max"] - col["x_min"]
-
-        # Narrow column with mostly short words -> MIGHT be inline markers
-        if col_words and col_width < 80:
-            avg_len = sum(len(w.get("text", "")) for w in col_words) / len(col_words)
-            if avg_len <= 2 and i + 1 < len(columns):
-                # Check if words are actual markers (symbols/numbers) vs
-                # real alphabetic words like "to", "in", "der", "die"
-                texts = [(w.get("text") or "").strip() for w in col_words]
-                alpha_count = sum(
-                    1 for t in texts
-                    if t and t[0].isalpha() and t not in _MARKER_CHARS
-                )
-                alpha_ratio = alpha_count / len(texts) if texts else 0
-
-                # If >=50% of words are alphabetic, this is a real column
-                if alpha_ratio >= 0.5:
-                    logger.info(
-                        "  kept narrow column %d (w=%d, avg_len=%.1f, "
-                        "alpha=%.0f%%) -- contains real words",
-                        i, col_width, avg_len, alpha_ratio * 100,
-                    )
-                else:
-                    # Merge into next column
-                    next_col = columns[i + 1].copy()
-                    next_col["x_min"] = col["x_min"]
-                    merged.append(next_col)
-                    skip.add(i + 1)
-                    logger.info(
-                        "  merged inline marker column %d (w=%d, avg_len=%.1f) "
-                        "into column %d",
-                        i, col_width, avg_len, i + 1,
-                    )
-                    continue
-
-        merged.append(col)
-
-    # Re-index
-    for i, col in enumerate(merged):
-        col["index"] = i
-        col["type"] = f"column_{i + 1}" if len(merged) > 1 else "column_text"
-
-    return merged

klausur-service/backend/grid_editor_filters.py

@@ -1,402 +1,4 @@
-"""
+# Backward-compat shim -- module moved to grid/editor/filters.py
-Grid Editor — word/zone filtering, border ghosts, decorative margins, footers.
+import importlib as _importlib
-
+import sys as _sys
-Split from grid_editor_helpers.py for maintainability.
+_sys.modules[__name__] = _importlib.import_module("grid.editor.filters")
-All functions are pure computation — no HTTP, DB, or session side effects.
-
-Lizenz: Apache 2.0 (kommerziell nutzbar)
-DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
-"""
-
-import logging
-from typing import Any, Dict, List, Optional, Tuple
-
-logger = logging.getLogger(__name__)
-
-
-def _filter_border_strip_words(words: List[Dict]) -> Tuple[List[Dict], int]:
-    """Remove page-border decoration strip words BEFORE column detection.
-
-    Scans from each page edge inward to find the first significant x-gap
-    (>30 px).  If the edge cluster contains <15 % of total words, those
-    words are removed as border-strip artifacts (alphabet letters,
-    illustration fragments).
-
-    Must run BEFORE ``_build_zone_grid`` so that column detection only
-    sees real content words and doesn't produce inflated row counts.
-    """
-    if len(words) < 10:
-        return words, 0
-
-    sorted_words = sorted(words, key=lambda w: w.get("left", 0))
-    total = len(sorted_words)
-
-    # -- Left-edge scan (running max right-edge) --
-    left_count = 0
-    running_right = 0
-    for gi in range(total - 1):
-        running_right = max(
-            running_right,
-            sorted_words[gi].get("left", 0) + sorted_words[gi].get("width", 0),
-        )
-        if sorted_words[gi + 1].get("left", 0) - running_right > 30:
-            left_count = gi + 1
-            break
-
-    # -- Right-edge scan (running min left) --
-    right_count = 0
-    running_left = sorted_words[-1].get("left", 0)
-    for gi in range(total - 1, 0, -1):
-        running_left = min(running_left, sorted_words[gi].get("left", 0))
-        prev_right = (
-            sorted_words[gi - 1].get("left", 0)
-            + sorted_words[gi - 1].get("width", 0)
-        )
-        if running_left - prev_right > 30:
-            right_count = total - gi
-            break
-
-    # Validate candidate strip: real border decorations are mostly short
-    # words (alphabet letters like "A", "Bb", stray marks).  Multi-word
-    # content like "der Ranzen" or "die Schals" (continuation of German
-    # translations) must NOT be removed.
-    def _is_decorative_strip(candidates: List[Dict]) -> bool:
-        if not candidates:
-            return False
-        short = sum(1 for w in candidates if len((w.get("text") or "").strip()) <= 2)
-        return short / len(candidates) >= 0.45
-
-    strip_ids: set = set()
-    if left_count > 0 and left_count / total < 0.20:
-        candidates = sorted_words[:left_count]
-        if _is_decorative_strip(candidates):
-            strip_ids = {id(w) for w in candidates}
-    elif right_count > 0 and right_count / total < 0.20:
-        candidates = sorted_words[total - right_count:]
-        if _is_decorative_strip(candidates):
-            strip_ids = {id(w) for w in candidates}
-
-    if not strip_ids:
-        return words, 0
-
-    return [w for w in words if id(w) not in strip_ids], len(strip_ids)
-
-
-# Characters that are typically OCR artefacts from box border lines.
-# Intentionally excludes ! (red markers) and . , ; (real punctuation).
-_GRID_GHOST_CHARS = set("|1lI[](){}/\\-\u2014\u2013_~=+")
-
-
-def _filter_border_ghosts(
-    words: List[Dict],
-    boxes: List,
-) -> tuple:
-    """Remove words sitting on box borders that are OCR artefacts.
-
-    Returns (filtered_words, removed_count).
-    """
-    if not boxes or not words:
-        return words, 0
-
-    # Build border bands from detected boxes
-    x_bands: List[tuple] = []
-    y_bands: List[tuple] = []
-    for b in boxes:
-        bt = (
-            b.border_thickness
-            if hasattr(b, "border_thickness")
-            else b.get("border_thickness", 3)
-        )
-        # Skip borderless boxes (images/graphics) -- no border line to produce ghosts
-        if bt == 0:
-            continue
-        bx = b.x if hasattr(b, "x") else b.get("x", 0)
-        by = b.y if hasattr(b, "y") else b.get("y", 0)
-        bw = b.width if hasattr(b, "width") else b.get("w", b.get("width", 0))
-        bh = b.height if hasattr(b, "height") else b.get("h", b.get("height", 0))
-        margin = max(bt * 2, 10) + 6
-        x_bands.append((bx - margin, bx + margin))
-        x_bands.append((bx + bw - margin, bx + bw + margin))
-        y_bands.append((by - margin, by + margin))
-        y_bands.append((by + bh - margin, by + bh + margin))
-
-    def _is_ghost(w: Dict) -> bool:
-        text = (w.get("text") or "").strip()
-        if not text:
-            return False
-        # Check if any word edge (not just center) touches a border band
-        w_left = w["left"]
-        w_right = w["left"] + w["width"]
-        w_top = w["top"]
-        w_bottom = w["top"] + w["height"]
-        on_border = (
-            any(lo <= w_left <= hi or lo <= w_right <= hi for lo, hi in x_bands)
-            or any(lo <= w_top <= hi or lo <= w_bottom <= hi for lo, hi in y_bands)
-        )
-        if not on_border:
-            return False
-        if len(text) == 1 and text in _GRID_GHOST_CHARS:
-            return True
-        return False
-
-    filtered = [w for w in words if not _is_ghost(w)]
-    return filtered, len(words) - len(filtered)
-
-
-def _flatten_word_boxes(cells: List[Dict]) -> List[Dict]:
-    """Extract all word_boxes from cells into a flat list of word dicts."""
-    words: List[Dict] = []
-    for cell in cells:
-        for wb in cell.get("word_boxes") or []:
-            if wb.get("text", "").strip():
-                words.append({
-                    "text": wb["text"],
-                    "left": wb["left"],
-                    "top": wb["top"],
-                    "width": wb["width"],
-                    "height": wb["height"],
-                    "conf": wb.get("conf", 0),
-                })
-    return words
-
-
-def _words_in_zone(
-    words: List[Dict],
-    zone_y: int,
-    zone_h: int,
-    zone_x: int,
-    zone_w: int,
-) -> List[Dict]:
-    """Filter words whose Y-center falls within a zone's bounds."""
-    zone_y_end = zone_y + zone_h
-    zone_x_end = zone_x + zone_w
-    result = []
-    for w in words:
-        cy = w["top"] + w["height"] / 2
-        cx = w["left"] + w["width"] / 2
-        if zone_y <= cy <= zone_y_end and zone_x <= cx <= zone_x_end:
-            result.append(w)
-    return result
-
-
-def _get_content_bounds(words: List[Dict]) -> tuple:
-    """Get content bounds from word positions."""
-    if not words:
-        return 0, 0, 0, 0
-    x_min = min(w["left"] for w in words)
-    y_min = min(w["top"] for w in words)
-    x_max = max(w["left"] + w["width"] for w in words)
-    y_max = max(w["top"] + w["height"] for w in words)
-    return x_min, y_min, x_max - x_min, y_max - y_min
-
-
-def _filter_decorative_margin(
-    words: List[Dict],
-    img_w: int,
-    log: Any,
-    session_id: str,
-) -> Dict[str, Any]:
-    """Remove words that belong to a decorative alphabet strip on a margin.
-
-    Some vocabulary worksheets have a vertical A-Z alphabet graphic along
-    the left or right edge.  OCR reads each letter as an isolated single-
-    character word.  These decorative elements are not content and confuse
-    column/row detection.
-
-    Detection criteria (phase 1 -- find the strip using single-char words):
-      - Words are in the outer 30% of the page (left or right)
-      - Nearly all words are single characters (letters or digits)
-      - At least 8 such words form a vertical strip (>=8 unique Y positions)
-      - Average horizontal spread of the strip is small (< 80px)
-
-    Phase 2 -- once a strip is confirmed, also remove any short word (<=3
-    chars) in the same narrow x-range.  This catches multi-char OCR
-    artifacts like "Vv" that belong to the same decorative element.
-
-    Modifies *words* in place.
-
-    Returns:
-        Dict with 'found' (bool), 'side' (str), 'letters_detected' (int).
-    """
-    no_strip: Dict[str, Any] = {"found": False, "side": "", "letters_detected": 0}
-    if not words or img_w <= 0:
-        return no_strip
-
-    margin_cutoff = img_w * 0.30
-    # Phase 1: find candidate strips using short words (1-2 chars).
-    # OCR often reads alphabet sidebar letters as pairs ("Aa", "Bb")
-    # rather than singles, so accept <=2-char words as strip candidates.
-    left_strip = [
-        w for w in words
-        if len((w.get("text") or "").strip()) <= 2
-        and w["left"] + w.get("width", 0) / 2 < margin_cutoff
-    ]
-    right_strip = [
-        w for w in words
-        if len((w.get("text") or "").strip()) <= 2
-        and w["left"] + w.get("width", 0) / 2 > img_w - margin_cutoff
-    ]
-
-    for strip, side in [(left_strip, "left"), (right_strip, "right")]:
-        if len(strip) < 6:
-            continue
-        # Check vertical distribution: should have many distinct Y positions
-        y_centers = sorted(set(
-            int(w["top"] + w.get("height", 0) / 2) // 20 * 20  # bucket
-            for w in strip
-        ))
-        if len(y_centers) < 6:
-            continue
-        # Check horizontal compactness
-        x_positions = [w["left"] for w in strip]
-        x_min = min(x_positions)
-        x_max = max(x_positions)
-        x_spread = x_max - x_min
-        if x_spread > 80:
-            continue
-
-        # Phase 2: strip confirmed -- also collect short words in same x-range
-        # Expand x-range slightly to catch neighbors (e.g. "Vv" next to "U")
-        strip_x_lo = x_min - 20
-        strip_x_hi = x_max + 60  # word width + tolerance
-        all_strip_words = [
-            w for w in words
-            if len((w.get("text") or "").strip()) <= 3
-            and strip_x_lo <= w["left"] <= strip_x_hi
-            and (w["left"] + w.get("width", 0) / 2 < margin_cutoff
-                 if side == "left"
-                 else w["left"] + w.get("width", 0) / 2 > img_w - margin_cutoff)
-        ]
-
-        strip_set = set(id(w) for w in all_strip_words)
-        before = len(words)
-        words[:] = [w for w in words if id(w) not in strip_set]
-        removed = before - len(words)
-        if removed:
-            log.info(
-                "build-grid session %s: removed %d decorative %s-margin words "
-                "(strip x=%d-%d)",
-                session_id, removed, side, strip_x_lo, strip_x_hi,
-            )
-        return {"found": True, "side": side, "letters_detected": len(strip)}
-
-    return no_strip
-
-
-def _filter_footer_words(
-    words: List[Dict],
-    img_h: int,
-    log: Any,
-    session_id: str,
-) -> Optional[Dict]:
-    """Remove isolated words in the bottom 5% of the page (page numbers).
-
-    Modifies *words* in place and returns a page_number metadata dict
-    if a page number was extracted, or None.
-    """
-    if not words or img_h <= 0:
-        return None
-    footer_y = img_h * 0.95
-    footer_words = [
-        w for w in words
-        if w["top"] + w.get("height", 0) / 2 > footer_y
-    ]
-    if not footer_words:
-        return None
-    # Only remove if footer has very few words (<= 3) with short text
-    total_text = "".join((w.get("text") or "").strip() for w in footer_words)
-    if len(footer_words) <= 3 and len(total_text) <= 10:
-        # Extract page number metadata before removing
-        page_number_info = {
-            "text": total_text.strip(),
-            "y_pct": round(footer_words[0]["top"] / img_h * 100, 1),
-        }
-        # Try to parse as integer
-        digits = "".join(c for c in total_text if c.isdigit())
-        if digits:
-            page_number_info["number"] = int(digits)
-
-        footer_set = set(id(w) for w in footer_words)
-        words[:] = [w for w in words if id(w) not in footer_set]
-        log.info(
-            "build-grid session %s: extracted page number '%s' and removed %d footer words",
-            session_id, total_text, len(footer_words),
-        )
-        return page_number_info
-    return None
-
-
-def _filter_header_junk(
-    words: List[Dict],
-    img_h: int,
-    log: Any,
-    session_id: str,
-) -> None:
-    """Remove OCR junk from header illustrations above the real content.
-
-    Textbook pages often have decorative header graphics (illustrations,
-    icons) that OCR reads as low-confidence junk characters.  Real content
-    typically starts further down the page.
-
-    Algorithm:
-      1. Find the "content start" -- the first Y position where a dense
-         horizontal row of 3+ high-confidence words begins.
-      2. Above that line, remove words with conf < 75 and text <= 3 chars.
-         These are almost certainly OCR artifacts from illustrations.
-
-    Modifies *words* in place.
-    """
-    if not words or img_h <= 0:
-        return
-
-    # --- Find content start: first horizontal row with >=3 high-conf words ---
-    # Sort words by Y
-    sorted_by_y = sorted(words, key=lambda w: w["top"])
-    content_start_y = 0
-    _ROW_TOLERANCE = img_h * 0.02  # words within 2% of page height = same row
-    _MIN_ROW_WORDS = 3
-    _MIN_CONF = 80
-
-    i = 0
-    while i < len(sorted_by_y):
-        row_y = sorted_by_y[i]["top"]
-        # Collect words in this row band
-        row_words = []
-        j = i
-        while j < len(sorted_by_y) and sorted_by_y[j]["top"] - row_y < _ROW_TOLERANCE:
-            row_words.append(sorted_by_y[j])
-            j += 1
-        # Count high-confidence words with real text (> 1 char)
-        high_conf = [
-            w for w in row_words
-            if w.get("conf", 0) >= _MIN_CONF
-            and len((w.get("text") or "").strip()) > 1
-        ]
-        if len(high_conf) >= _MIN_ROW_WORDS:
-            content_start_y = row_y
-            break
-        i = j if j > i else i + 1
-
-    if content_start_y <= 0:
-        return  # no clear content start found
-
-    # --- Remove low-conf short junk above content start ---
-    junk = [
-        w for w in words
-        if w["top"] + w.get("height", 0) < content_start_y
-        and w.get("conf", 0) < 75
-        and len((w.get("text") or "").strip()) <= 3
-    ]
-    if not junk:
-        return
-
-    junk_set = set(id(w) for w in junk)
-    before = len(words)
-    words[:] = [w for w in words if id(w) not in junk_set]
-    removed = before - len(words)
-    if removed:
-        log.info(
-            "build-grid session %s: removed %d header junk words above y=%d "
-            "(content start)",
-            session_id, removed, content_start_y,
-        )

klausur-service/backend/grid_editor_headers.py

@@ -1,499 +1,4 @@
-"""
+# Backward-compat shim -- module moved to grid/editor/headers.py
-Grid Editor — header/heading detection and colspan (merged cell) detection.
+import importlib as _importlib
-Split from grid_editor_helpers.py.  Pure computation, no HTTP/DB side effects.
+import sys as _sys
-Lizenz: Apache 2.0 | DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+_sys.modules[__name__] = _importlib.import_module("grid.editor.headers")
-"""
-
-import logging
-import re
-from typing import Any, Dict, List, Optional
-
-from cv_ocr_engines import _text_has_garbled_ipa
-
-logger = logging.getLogger(__name__)
-
-
-def _detect_heading_rows_by_color(zones_data: List[Dict], img_w: int, img_h: int) -> int:
-    """Detect heading rows by color + height after color annotation.
-
-    A row is a heading if:
-    1. ALL word_boxes have color_name != 'black' (typically 'blue')
-    2. Mean word height > 1.2x median height of all words in the zone
-
-    Detected heading rows are merged into a single spanning cell.
-    Returns count of headings detected.
-    """
-    heading_count = 0
-
-    for z in zones_data:
-        cells = z.get("cells", [])
-        rows = z.get("rows", [])
-        columns = z.get("columns", [])
-        if not cells or not rows or len(columns) < 2:
-            continue
-
-        # Compute median word height across the zone
-        all_heights = []
-        for cell in cells:
-            for wb in cell.get("word_boxes") or []:
-                h = wb.get("height", 0)
-                if h > 0:
-                    all_heights.append(h)
-        if not all_heights:
-            continue
-        all_heights_sorted = sorted(all_heights)
-        median_h = all_heights_sorted[len(all_heights_sorted) // 2]
-
-        heading_row_indices = []
-        for row in rows:
-            if row.get("is_header"):
-                continue  # already detected as header
-            ri = row["index"]
-            row_cells = [c for c in cells if c.get("row_index") == ri]
-            row_wbs = [
-                wb for cell in row_cells
-                for wb in cell.get("word_boxes") or []
-            ]
-            if not row_wbs:
-                continue
-
-            # Condition 1: ALL words are non-black
-            all_colored = all(
-                wb.get("color_name", "black") != "black"
-                for wb in row_wbs
-            )
-            if not all_colored:
-                continue
-
-            # Condition 2: mean height > 1.2x median
-            mean_h = sum(wb.get("height", 0) for wb in row_wbs) / len(row_wbs)
-            if mean_h <= median_h * 1.2:
-                continue
-
-            heading_row_indices.append(ri)
-
-        # Merge heading cells into spanning cells
-        for hri in heading_row_indices:
-            header_cells = [c for c in cells if c.get("row_index") == hri]
-            if len(header_cells) <= 1:
-                # Single cell -- just mark it as heading
-                if header_cells:
-                    header_cells[0]["col_type"] = "heading"
-                    heading_count += 1
-                    # Mark row as header
-                    for row in rows:
-                        if row["index"] == hri:
-                            row["is_header"] = True
-                continue
-
-            # Collect all word_boxes and text from all columns
-            all_wb = []
-            all_text_parts = []
-            for hc in sorted(header_cells, key=lambda c: c["col_index"]):
-                all_wb.extend(hc.get("word_boxes", []))
-                if hc.get("text", "").strip():
-                    all_text_parts.append(hc["text"].strip())
-
-            # Remove all cells for this row, replace with one spanning cell
-            z["cells"] = [c for c in z["cells"] if c.get("row_index") != hri]
-
-            if all_wb:
-                x_min = min(wb["left"] for wb in all_wb)
-                y_min = min(wb["top"] for wb in all_wb)
-                x_max = max(wb["left"] + wb["width"] for wb in all_wb)
-                y_max = max(wb["top"] + wb["height"] for wb in all_wb)
-
-                # Use the actual starting col_index from the first cell
-                first_col = min(hc["col_index"] for hc in header_cells)
-                zone_idx = z.get("zone_index", 0)
-                z["cells"].append({
-                    "cell_id": f"Z{zone_idx}_R{hri:02d}_C{first_col}",
-                    "zone_index": zone_idx,
-                    "row_index": hri,
-                    "col_index": first_col,
-                    "col_type": "heading",
-                    "text": " ".join(all_text_parts),
-                    "confidence": 0.0,
-                    "bbox_px": {"x": x_min, "y": y_min,
-                                "w": x_max - x_min, "h": y_max - y_min},
-                    "bbox_pct": {
-                        "x": round(x_min / img_w * 100, 2) if img_w else 0,
-                        "y": round(y_min / img_h * 100, 2) if img_h else 0,
-                        "w": round((x_max - x_min) / img_w * 100, 2) if img_w else 0,
-                        "h": round((y_max - y_min) / img_h * 100, 2) if img_h else 0,
-                    },
-                    "word_boxes": all_wb,
-                    "ocr_engine": "words_first",
-                    "is_bold": True,
-                })
-
-            # Mark row as header
-            for row in rows:
-                if row["index"] == hri:
-                    row["is_header"] = True
-            heading_count += 1
-
-    return heading_count
-
-
-def _detect_heading_rows_by_single_cell(
-    zones_data: List[Dict], img_w: int, img_h: int,
-) -> int:
-    """Detect heading rows that have only a single content cell.
-
-    Black headings like "Theme" have normal color and height, so they are
-    missed by ``_detect_heading_rows_by_color``.  The distinguishing signal
-    is that they occupy only one column while normal vocabulary rows fill
-    at least 2-3 columns.
-
-    A row qualifies as a heading if:
-    1. It is not already marked as a header/heading.
-    2. It has exactly ONE cell whose col_type starts with ``column_``
-       (excluding column_1 / page_ref which only carries page numbers).
-    3. That single cell is NOT in the last column (continuation/example
-       lines like "2. Ver\u00e4nderung, Wechsel" often sit alone in column_4).
-    4. The text does not start with ``[`` (IPA continuation).
-    5. The zone has >=3 columns and >=5 rows (avoids false positives in
-       tiny zones).
-    6. The majority of rows in the zone have >=2 content cells (ensures
-       we are in a multi-column vocab layout).
-    """
-    heading_count = 0
-
-    for z in zones_data:
-        cells = z.get("cells", [])
-        rows = z.get("rows", [])
-        columns = z.get("columns", [])
-        if len(columns) < 3 or len(rows) < 5:
-            continue
-
-        # Determine the last col_index (example/sentence column)
-        col_indices = sorted(set(c.get("col_index", 0) for c in cells))
-        if not col_indices:
-            continue
-        last_col = col_indices[-1]
-
-        # Count content cells per row (column_* but not column_1/page_ref).
-        # Exception: column_1 cells that contain a dictionary article word
-        # (die/der/das etc.) ARE content -- they appear in dictionary layouts
-        # where the leftmost column holds grammatical articles.
-        _ARTICLE_WORDS = {
-            "die", "der", "das", "dem", "den", "des", "ein", "eine",
-            "the", "a", "an",
-        }
-        row_content_counts: Dict[int, int] = {}
-        for cell in cells:
-            ct = cell.get("col_type", "")
-            if not ct.startswith("column_"):
-                continue
-            if ct == "column_1":
-                ctext = (cell.get("text") or "").strip().lower()
-                if ctext not in _ARTICLE_WORDS:
-                    continue
-            ri = cell.get("row_index", -1)
-            row_content_counts[ri] = row_content_counts.get(ri, 0) + 1
-
-        # Majority of rows must have >=2 content cells
-        multi_col_rows = sum(1 for cnt in row_content_counts.values() if cnt >= 2)
-        if multi_col_rows < len(rows) * 0.4:
-            continue
-
-        # Exclude first and last non-header rows -- these are typically
-        # page numbers or footer text, not headings.
-        non_header_rows = [r for r in rows if not r.get("is_header")]
-        if len(non_header_rows) < 3:
-            continue
-        first_ri = non_header_rows[0]["index"]
-        last_ri = non_header_rows[-1]["index"]
-
-        heading_row_indices = []
-        for row in rows:
-            if row.get("is_header"):
-                continue
-            ri = row["index"]
-            if ri == first_ri or ri == last_ri:
-                continue
-            row_cells = [c for c in cells if c.get("row_index") == ri]
-            content_cells = [
-                c for c in row_cells
-                if c.get("col_type", "").startswith("column_")
-                and (c.get("col_type") != "column_1"
-                     or (c.get("text") or "").strip().lower() in _ARTICLE_WORDS)
-            ]
-            if len(content_cells) != 1:
-                continue
-            cell = content_cells[0]
-            # Not in the last column (continuation/example lines)
-            if cell.get("col_index") == last_col:
-                continue
-            text = (cell.get("text") or "").strip()
-            if not text or text.startswith("["):
-                continue
-            # Continuation lines start with "(" -- e.g. "(usw.)", "(TV-Serie)"
-            if text.startswith("("):
-                continue
-            # Single cell NOT in the first content column is likely a
-            # continuation/overflow line, not a heading.  Real headings
-            # ("Theme 1", "Unit 3: ...") appear in the first or second
-            # content column.
-            first_content_col = col_indices[0] if col_indices else 0
-            if cell.get("col_index", 0) > first_content_col + 1:
-                continue
-            # Skip garbled IPA without brackets (e.g. "ska:f -- ska:vz")
-            # but NOT text with real IPA symbols (e.g. "Theme [\u03b8\u02c8i\u02d0m]")
-            _REAL_IPA_CHARS = set("\u02c8\u02cc\u0259\u026a\u025b\u0252\u028a\u028c\u00e6\u0251\u0254\u0283\u0292\u03b8\u00f0\u014b")
-            if _text_has_garbled_ipa(text) and not any(c in _REAL_IPA_CHARS for c in text):
-                continue
-            # Guard: dictionary section headings are short (1-4 alpha chars
-            # like "A", "Ab", "Zi", "Sch").  Longer text that starts
-            # lowercase is a regular vocabulary word (e.g. "zentral") that
-            # happens to appear alone in its row.
-            alpha_only = re.sub(r'[^a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df\u1e9e]', '', text)
-            if len(alpha_only) > 4 and text[0].islower():
-                continue
-            heading_row_indices.append(ri)
-
-        # Guard: if >25% of eligible rows would become headings, the
-        # heuristic is misfiring (e.g. sparse single-column layout where
-        # most rows naturally have only 1 content cell).
-        eligible_rows = len(non_header_rows) - 2  # minus first/last excluded
-        if eligible_rows > 0 and len(heading_row_indices) > eligible_rows * 0.25:
-            logger.debug(
-                "Skipping single-cell heading detection for zone %s: "
-                "%d/%d rows would be headings (>25%%)",
-                z.get("zone_index"), len(heading_row_indices), eligible_rows,
-            )
-            continue
-
-        for hri in heading_row_indices:
-            header_cells = [c for c in cells if c.get("row_index") == hri]
-            if not header_cells:
-                continue
-
-            # Collect all word_boxes and text
-            all_wb = []
-            all_text_parts = []
-            for hc in sorted(header_cells, key=lambda c: c["col_index"]):
-                all_wb.extend(hc.get("word_boxes", []))
-                if hc.get("text", "").strip():
-                    all_text_parts.append(hc["text"].strip())
-
-            first_col_idx = min(hc["col_index"] for hc in header_cells)
-
-            # Remove old cells for this row, add spanning heading cell
-            z["cells"] = [c for c in z["cells"] if c.get("row_index") != hri]
-
-            if all_wb:
-                x_min = min(wb["left"] for wb in all_wb)
-                y_min = min(wb["top"] for wb in all_wb)
-                x_max = max(wb["left"] + wb["width"] for wb in all_wb)
-                y_max = max(wb["top"] + wb["height"] for wb in all_wb)
-            else:
-                # Fallback to first cell bbox
-                bp = header_cells[0].get("bbox_px", {})
-                x_min = bp.get("x", 0)
-                y_min = bp.get("y", 0)
-                x_max = x_min + bp.get("w", 0)
-                y_max = y_min + bp.get("h", 0)
-
-            zone_idx = z.get("zone_index", 0)
-            z["cells"].append({
-                "cell_id": f"Z{zone_idx}_R{hri:02d}_C{first_col_idx}",
-                "zone_index": zone_idx,
-                "row_index": hri,
-                "col_index": first_col_idx,
-                "col_type": "heading",
-                "text": " ".join(all_text_parts),
-                "confidence": 0.0,
-                "bbox_px": {"x": x_min, "y": y_min,
-                            "w": x_max - x_min, "h": y_max - y_min},
-                "bbox_pct": {
-                    "x": round(x_min / img_w * 100, 2) if img_w else 0,
-                    "y": round(y_min / img_h * 100, 2) if img_h else 0,
-                    "w": round((x_max - x_min) / img_w * 100, 2) if img_w else 0,
-                    "h": round((y_max - y_min) / img_h * 100, 2) if img_h else 0,
-                },
-                "word_boxes": all_wb,
-                "ocr_engine": "words_first",
-                "is_bold": False,
-            })
-
-            for row in rows:
-                if row["index"] == hri:
-                    row["is_header"] = True
-            heading_count += 1
-
-    return heading_count
-
-
-def _detect_header_rows(
-    rows: List[Dict],
-    zone_words: List[Dict],
-    zone_y: int,
-    columns: Optional[List[Dict]] = None,
-    skip_first_row_header: bool = False,
-) -> List[int]:
-    """Detect header rows: first-row heuristic + spanning header detection.
-
-    A "spanning header" is a row whose words stretch across multiple column
-    boundaries (e.g. "Unit4: Bonnie Scotland" centred across 4 columns).
-    """
-    if len(rows) < 2:
-        return []
-
-    headers = []
-
-    if not skip_first_row_header:
-        first_row = rows[0]
-        second_row = rows[1]
-
-        # Gap between first and second row > 0.5x average row height
-        avg_h = sum(r["y_max"] - r["y_min"] for r in rows) / len(rows)
-        gap = second_row["y_min"] - first_row["y_max"]
-        if gap > avg_h * 0.5:
-            headers.append(0)
-
-        # Also check if first row words are taller than average (bold/header text)
-        all_heights = [w["height"] for w in zone_words]
-        median_h = sorted(all_heights)[len(all_heights) // 2] if all_heights else 20
-        first_row_words = [
-            w for w in zone_words
-            if first_row["y_min"] <= w["top"] + w["height"] / 2 <= first_row["y_max"]
-        ]
-        if first_row_words:
-            first_h = max(w["height"] for w in first_row_words)
-            if first_h > median_h * 1.3:
-                if 0 not in headers:
-                    headers.append(0)
-
-    # Note: Spanning-header detection (rows spanning all columns) has been
-    # disabled because it produces too many false positives on vocabulary
-    # worksheets where IPA transcriptions or short entries naturally span
-    # multiple columns with few words.  The first-row heuristic above is
-    # sufficient for detecting real headers.
-
-    return headers
-
-
-def _detect_colspan_cells(
-    zone_words: List[Dict],
-    columns: List[Dict],
-    rows: List[Dict],
-    cells: List[Dict],
-    img_w: int,
-    img_h: int,
-) -> List[Dict]:
-    """Detect and merge cells that span multiple columns (colspan).
-
-    A word-block (PaddleOCR phrase) that extends significantly past a column
-    boundary into the next column indicates a merged cell.  This replaces
-    the incorrectly split cells with a single cell spanning multiple columns.
-
-    Works for both full-page scans and box zones.
-    """
-    if len(columns) < 2 or not zone_words or not rows:
-        return cells
-
-    from cv_words_first import _assign_word_to_row
-
-    # Column boundaries (midpoints between adjacent columns)
-    col_boundaries = []
-    for ci in range(len(columns) - 1):
-        col_boundaries.append((columns[ci]["x_max"] + columns[ci + 1]["x_min"]) / 2)
-
-    def _cols_covered(w_left: float, w_right: float) -> List[int]:
-        """Return list of column indices that a word-block covers."""
-        covered = []
-        for col in columns:
-            col_mid = (col["x_min"] + col["x_max"]) / 2
-            # Word covers a column if it extends past the column's midpoint
-            if w_left < col_mid < w_right:
-                covered.append(col["index"])
-            # Also include column if word starts within it
-            elif col["x_min"] <= w_left < col["x_max"]:
-                covered.append(col["index"])
-        return sorted(set(covered))
-
-    # Group original word-blocks by row
-    row_word_blocks: Dict[int, List[Dict]] = {}
-    for w in zone_words:
-        ri = _assign_word_to_row(w, rows)
-        row_word_blocks.setdefault(ri, []).append(w)
-
-    # For each row, check if any word-block spans multiple columns
-    rows_to_merge: Dict[int, List[Dict]] = {}  # row_index -> list of spanning word-blocks
-
-    for ri, wblocks in row_word_blocks.items():
-        spanning = []
-        for w in wblocks:
-            w_left = w["left"]
-            w_right = w_left + w["width"]
-            covered = _cols_covered(w_left, w_right)
-            if len(covered) >= 2:
-                spanning.append({"word": w, "cols": covered})
-        if spanning:
-            rows_to_merge[ri] = spanning
-
-    if not rows_to_merge:
-        return cells
-
-    # Merge cells for spanning rows
-    new_cells = []
-    for cell in cells:
-        ri = cell.get("row_index", -1)
-        if ri not in rows_to_merge:
-            new_cells.append(cell)
-            continue
-
-        # Check if this cell's column is part of a spanning block
-        ci = cell.get("col_index", -1)
-        is_part_of_span = False
-        for span in rows_to_merge[ri]:
-            if ci in span["cols"]:
-                is_part_of_span = True
-                # Only emit the merged cell for the FIRST column in the span
-                if ci == span["cols"][0]:
-                    # Use the ORIGINAL word-block text (not the split cell texts
-                    # which may have broken words like "euros a" + "nd cents")
-                    orig_word = span["word"]
-                    merged_text = orig_word.get("text", "").strip()
-                    all_wb = [orig_word]
-
-                    # Compute merged bbox
-                    if all_wb:
-                        x_min = min(wb["left"] for wb in all_wb)
-                        y_min = min(wb["top"] for wb in all_wb)
-                        x_max = max(wb["left"] + wb["width"] for wb in all_wb)
-                        y_max = max(wb["top"] + wb["height"] for wb in all_wb)
-                    else:
-                        x_min = y_min = x_max = y_max = 0
-
-                    new_cells.append({
-                        "cell_id": cell["cell_id"],
-                        "row_index": ri,
-                        "col_index": span["cols"][0],
-                        "col_type": "spanning_header",
-                        "colspan": len(span["cols"]),
-                        "text": merged_text,
-                        "confidence": cell.get("confidence", 0),
-                        "bbox_px": {"x": x_min, "y": y_min,
-                                    "w": x_max - x_min, "h": y_max - y_min},
-                        "bbox_pct": {
-                            "x": round(x_min / img_w * 100, 2) if img_w else 0,
-                            "y": round(y_min / img_h * 100, 2) if img_h else 0,
-                            "w": round((x_max - x_min) / img_w * 100, 2) if img_w else 0,
-                            "h": round((y_max - y_min) / img_h * 100, 2) if img_h else 0,
-                        },
-                        "word_boxes": all_wb,
-                        "ocr_engine": cell.get("ocr_engine", ""),
-                        "is_bold": cell.get("is_bold", False),
-                    })
-                    logger.info(
-                        "colspan detected: row %d, cols %s -> merged %d cells (%r)",
-                        ri, span["cols"], len(span["cols"]), merged_text[:50],
-                    )
-                break
-        if not is_part_of_span:
-            new_cells.append(cell)
-
-    return new_cells

klausur-service/backend/grid_editor_helpers.py

@@ -1,58 +1,4 @@
-"""
+# Backward-compat shim -- module moved to grid/editor/helpers.py
-Grid Editor helper functions — barrel re-export module.
+import importlib as _importlib
-
+import sys as _sys
-This file re-exports all public symbols from the split sub-modules
+_sys.modules[__name__] = _importlib.import_module("grid.editor.helpers")
-so that existing ``from grid_editor_helpers import ...`` statements
-continue to work without changes.
-
-Sub-modules:
-  - grid_editor_columns  — column detection, cross-column splitting, marker merging
-  - grid_editor_filters  — word/zone filtering, border ghosts, decorative margins
-  - grid_editor_headers  — header/heading detection, colspan detection
-  - grid_editor_zones    — vertical dividers, zone splitting/merging, zone grid building
-
-Lizenz: Apache 2.0 (kommerziell nutzbar)
-DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
-"""
-
-# --- Re-export: columns ---------------------------------------------------
-from grid_editor_columns import (  # noqa: F401
-    _is_recognized_word,
-    _split_cross_column_words,
-    _cluster_columns_by_alignment,
-    _MARKER_CHARS,
-    _merge_inline_marker_columns,
-)
-
-# --- Re-export: filters ----------------------------------------------------
-from grid_editor_filters import (  # noqa: F401
-    _filter_border_strip_words,
-    _GRID_GHOST_CHARS,
-    _filter_border_ghosts,
-    _flatten_word_boxes,
-    _words_in_zone,
-    _get_content_bounds,
-    _filter_decorative_margin,
-    _filter_footer_words,
-    _filter_header_junk,
-)
-
-# --- Re-export: headers ----------------------------------------------------
-from grid_editor_headers import (  # noqa: F401
-    _detect_heading_rows_by_color,
-    _detect_heading_rows_by_single_cell,
-    _detect_header_rows,
-    _detect_colspan_cells,
-)
-
-# --- Re-export: zones -------------------------------------------------------
-from grid_editor_zones import (  # noqa: F401
-    _PIPE_RE_VSPLIT,
-    _detect_vertical_dividers,
-    _split_zone_at_vertical_dividers,
-    _merge_content_zones_across_boxes,
-    _build_zone_grid,
-)
-
-# --- Re-export from cv_words_first (used by cv_box_layout.py) ---------------
-from cv_words_first import _cluster_rows  # noqa: F401

klausur-service/backend/grid_editor_zones.py

@@ -1,389 +1,4 @@
-"""
+# Backward-compat shim -- module moved to grid/editor/zones.py
-Grid Editor — vertical divider detection, zone splitting/merging, zone grid building.
+import importlib as _importlib
-
+import sys as _sys
-Split from grid_editor_helpers.py for maintainability.
+_sys.modules[__name__] = _importlib.import_module("grid.editor.zones")
-All functions are pure computation — no HTTP, DB, or session side effects.
-
-Lizenz: Apache 2.0 (kommerziell nutzbar)
-DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
-"""
-
-import logging
-import re
-from typing import Any, Dict, List, Optional
-
-from cv_vocab_types import PageZone
-from cv_words_first import _cluster_rows, _build_cells
-
-from grid_editor_columns import (
-    _cluster_columns_by_alignment,
-    _merge_inline_marker_columns,
-    _split_cross_column_words,
-)
-from grid_editor_headers import (
-    _detect_header_rows,
-    _detect_colspan_cells,
-)
-
-logger = logging.getLogger(__name__)
-
-
-# ---------------------------------------------------------------------------
-# Vertical divider detection and zone splitting
-# ---------------------------------------------------------------------------
-
-_PIPE_RE_VSPLIT = re.compile(r"^\|+$")
-
-
-def _detect_vertical_dividers(
-    words: List[Dict],
-    zone_x: int,
-    zone_w: int,
-    zone_y: int,
-    zone_h: int,
-) -> List[float]:
-    """Detect vertical divider lines from pipe word_boxes at consistent x.
-
-    Returns list of divider x-positions (empty if no dividers found).
-    """
-    if not words or zone_w <= 0 or zone_h <= 0:
-        return []
-
-    # Collect pipe word_boxes
-    pipes = [
-        w for w in words
-        if _PIPE_RE_VSPLIT.match((w.get("text") or "").strip())
-    ]
-    if len(pipes) < 5:
-        return []
-
-    # Cluster pipe x-centers by proximity
-    tolerance = max(15, int(zone_w * 0.02))
-    pipe_xs = sorted(w["left"] + w["width"] / 2 for w in pipes)
-
-    clusters: List[List[float]] = [[pipe_xs[0]]]
-    for x in pipe_xs[1:]:
-        if x - clusters[-1][-1] <= tolerance:
-            clusters[-1].append(x)
-        else:
-            clusters.append([x])
-
-    dividers: List[float] = []
-    for cluster in clusters:
-        if len(cluster) < 5:
-            continue
-        mean_x = sum(cluster) / len(cluster)
-        # Must be between 15% and 85% of zone width
-        rel_pos = (mean_x - zone_x) / zone_w
-        if rel_pos < 0.15 or rel_pos > 0.85:
-            continue
-        # Check vertical coverage: pipes must span >= 50% of zone height
-        cluster_pipes = [
-            w for w in pipes
-            if abs(w["left"] + w["width"] / 2 - mean_x) <= tolerance
-        ]
-        ys = [w["top"] for w in cluster_pipes] + [w["top"] + w["height"] for w in cluster_pipes]
-        y_span = max(ys) - min(ys) if ys else 0
-        if y_span < zone_h * 0.5:
-            continue
-        dividers.append(mean_x)
-
-    return sorted(dividers)
-
-
-def _split_zone_at_vertical_dividers(
-    zone: "PageZone",
-    divider_xs: List[float],
-    vsplit_group_id: int,
-) -> List["PageZone"]:
-    """Split a PageZone at vertical divider positions into sub-zones."""
-    boundaries = [zone.x] + divider_xs + [zone.x + zone.width]
-    hints = []
-    for i in range(len(boundaries) - 1):
-        if i == 0:
-            hints.append("left_of_vsplit")
-        elif i == len(boundaries) - 2:
-            hints.append("right_of_vsplit")
-        else:
-            hints.append("middle_of_vsplit")
-
-    sub_zones = []
-    for i in range(len(boundaries) - 1):
-        x_start = int(boundaries[i])
-        x_end = int(boundaries[i + 1])
-        sub = PageZone(
-            index=0,  # re-indexed later
-            zone_type=zone.zone_type,
-            y=zone.y,
-            height=zone.height,
-            x=x_start,
-            width=x_end - x_start,
-            box=zone.box,
-            image_overlays=zone.image_overlays,
-            layout_hint=hints[i],
-            vsplit_group=vsplit_group_id,
-        )
-        sub_zones.append(sub)
-
-    return sub_zones
-
-
-def _merge_content_zones_across_boxes(
-    zones: List,
-    content_x: int,
-    content_w: int,
-) -> List:
-    """Merge content zones separated by box zones into single zones.
-
-    Box zones become image_overlays on the merged content zone.
-    Pattern: [content, box*, content] -> [merged_content with overlay]
-    Box zones NOT between two content zones stay as standalone zones.
-    """
-    if len(zones) < 3:
-        return zones
-
-    # Group consecutive runs of [content, box+, content]
-    result: List = []
-    i = 0
-    while i < len(zones):
-        z = zones[i]
-        if z.zone_type != "content":
-            result.append(z)
-            i += 1
-            continue
-
-        # Start of a potential merge group: content zone
-        group_contents = [z]
-        group_boxes = []
-        j = i + 1
-        # Absorb [box, content] pairs -- only absorb a box if it's
-        # confirmed to be followed by another content zone.
-        while j < len(zones):
-            if (zones[j].zone_type == "box"
-                    and j + 1 < len(zones)
-                    and zones[j + 1].zone_type == "content"):
-                group_boxes.append(zones[j])
-                group_contents.append(zones[j + 1])
-                j += 2
-            else:
-                break
-
-        if len(group_contents) >= 2 and group_boxes:
-            # Merge: create one large content zone spanning all
-            y_min = min(c.y for c in group_contents)
-            y_max = max(c.y + c.height for c in group_contents)
-            overlays = []
-            for bz in group_boxes:
-                overlay = {
-                    "y": bz.y,
-                    "height": bz.height,
-                    "x": bz.x,
-                    "width": bz.width,
-                }
-                if bz.box:
-                    overlay["box"] = {
-                        "x": bz.box.x,
-                        "y": bz.box.y,
-                        "width": bz.box.width,
-                        "height": bz.box.height,
-                        "confidence": bz.box.confidence,
-                        "border_thickness": bz.box.border_thickness,
-                    }
-                overlays.append(overlay)
-
-            merged = PageZone(
-                index=0,  # re-indexed below
-                zone_type="content",
-                y=y_min,
-                height=y_max - y_min,
-                x=content_x,
-                width=content_w,
-                image_overlays=overlays,
-            )
-            result.append(merged)
-            i = j
-        else:
-            # No merge possible -- emit just the content zone
-            result.append(z)
-            i += 1
-
-    # Re-index zones
-    for idx, z in enumerate(result):
-        z.index = idx
-
-    logger.info(
-        "zone-merge: %d zones -> %d zones after merging across boxes",
-        len(zones), len(result),
-    )
-    return result
-
-
-def _build_zone_grid(
-    zone_words: List[Dict],
-    zone_x: int,
-    zone_y: int,
-    zone_w: int,
-    zone_h: int,
-    zone_index: int,
-    img_w: int,
-    img_h: int,
-    global_columns: Optional[List[Dict]] = None,
-    skip_first_row_header: bool = False,
-) -> Dict[str, Any]:
-    """Build columns, rows, cells for a single zone from its words.
-
-    Args:
-        global_columns: If provided, use these pre-computed column boundaries
-            instead of detecting columns per zone.  Used for content zones so
-            that all content zones (above/between/below boxes) share the same
-            column structure.  Box zones always detect columns independently.
-    """
-    if not zone_words:
-        return {
-            "columns": [],
-            "rows": [],
-            "cells": [],
-            "header_rows": [],
-        }
-
-    # Cluster rows first (needed for column alignment analysis)
-    rows = _cluster_rows(zone_words)
-
-    # Diagnostic logging for small/medium zones (box zones typically have 40-60 words)
-    if len(zone_words) <= 60:
-        import statistics as _st
-        _heights = [w['height'] for w in zone_words if w.get('height', 0) > 0]
-        _med_h = _st.median(_heights) if _heights else 20
-        _y_tol = max(_med_h * 0.5, 5)
-        logger.info(
-            "zone %d row-clustering: %d words, median_h=%.0f, y_tol=%.1f -> %d rows",
-            zone_index, len(zone_words), _med_h, _y_tol, len(rows),
-        )
-        for w in sorted(zone_words, key=lambda ww: (ww['top'], ww['left'])):
-            logger.info(
-                "  zone %d word: y=%d x=%d h=%d w=%d '%s'",
-                zone_index, w['top'], w['left'], w['height'], w['width'],
-                w.get('text', '')[:40],
-            )
-        for r in rows:
-            logger.info(
-                "  zone %d row %d: y_min=%d y_max=%d y_center=%.0f",
-                zone_index, r['index'], r['y_min'], r['y_max'], r['y_center'],
-            )
-
-    # Use global columns if provided, otherwise detect per zone
-    columns = global_columns if global_columns else _cluster_columns_by_alignment(zone_words, zone_w, rows)
-
-    # Merge inline marker columns (bullets, numbering) into adjacent text
-    if not global_columns:
-        columns = _merge_inline_marker_columns(columns, zone_words)
-
-    if not columns or not rows:
-        return {
-            "columns": [],
-            "rows": [],
-            "cells": [],
-            "header_rows": [],
-        }
-
-    # Split word boxes that straddle column boundaries (e.g. "sichzie"
-    # spanning Col 1 + Col 2).  Must happen after column detection and
-    # before cell assignment.
-    # Keep original words for colspan detection (split destroys span info).
-    original_zone_words = zone_words
-    if len(columns) >= 2:
-        zone_words = _split_cross_column_words(zone_words, columns)
-
-    # Build cells
-    cells = _build_cells(zone_words, columns, rows, img_w, img_h)
-
-    # --- Detect colspan (merged cells spanning multiple columns) ---
-    # Uses the ORIGINAL (pre-split) words to detect word-blocks that span
-    # multiple columns.  _split_cross_column_words would have destroyed
-    # this information by cutting words at column boundaries.
-    if len(columns) >= 2:
-        cells = _detect_colspan_cells(original_zone_words, columns, rows, cells, img_w, img_h)
-
-    # Prefix cell IDs with zone index
-    for cell in cells:
-        cell["cell_id"] = f"Z{zone_index}_{cell['cell_id']}"
-        cell["zone_index"] = zone_index
-
-    # Detect header rows (pass columns for spanning header detection)
-    header_rows = _detect_header_rows(rows, zone_words, zone_y, columns,
-                                      skip_first_row_header=skip_first_row_header)
-
-    # Merge cells in spanning header rows into a single col-0 cell
-    if header_rows and len(columns) >= 2:
-        for hri in header_rows:
-            header_cells = [c for c in cells if c["row_index"] == hri]
-            if len(header_cells) <= 1:
-                continue
-            # Collect all word_boxes and text from all columns
-            all_wb = []
-            all_text_parts = []
-            for hc in sorted(header_cells, key=lambda c: c["col_index"]):
-                all_wb.extend(hc.get("word_boxes", []))
-                if hc.get("text", "").strip():
-                    all_text_parts.append(hc["text"].strip())
-            # Remove all header cells, replace with one spanning cell
-            cells = [c for c in cells if c["row_index"] != hri]
-            if all_wb:
-                x_min = min(wb["left"] for wb in all_wb)
-                y_min = min(wb["top"] for wb in all_wb)
-                x_max = max(wb["left"] + wb["width"] for wb in all_wb)
-                y_max = max(wb["top"] + wb["height"] for wb in all_wb)
-                cells.append({
-                    "cell_id": f"R{hri:02d}_C0",
-                    "row_index": hri,
-                    "col_index": 0,
-                    "col_type": "spanning_header",
-                    "text": " ".join(all_text_parts),
-                    "confidence": 0.0,
-                    "bbox_px": {"x": x_min, "y": y_min,
-                                "w": x_max - x_min, "h": y_max - y_min},
-                    "bbox_pct": {
-                        "x": round(x_min / img_w * 100, 2) if img_w else 0,
-                        "y": round(y_min / img_h * 100, 2) if img_h else 0,
-                        "w": round((x_max - x_min) / img_w * 100, 2) if img_w else 0,
-                        "h": round((y_max - y_min) / img_h * 100, 2) if img_h else 0,
-                    },
-                    "word_boxes": all_wb,
-                    "ocr_engine": "words_first",
-                    "is_bold": True,
-                })
-
-    # Convert columns to output format with percentages
-    out_columns = []
-    for col in columns:
-        x_min = col["x_min"]
-        x_max = col["x_max"]
-        out_columns.append({
-            "index": col["index"],
-            "label": col["type"],
-            "x_min_px": round(x_min),
-            "x_max_px": round(x_max),
-            "x_min_pct": round(x_min / img_w * 100, 2) if img_w else 0,
-            "x_max_pct": round(x_max / img_w * 100, 2) if img_w else 0,
-            "bold": False,
-        })
-
-    # Convert rows to output format with percentages
-    out_rows = []
-    for row in rows:
-        out_rows.append({
-            "index": row["index"],
-            "y_min_px": round(row["y_min"]),
-            "y_max_px": round(row["y_max"]),
-            "y_min_pct": round(row["y_min"] / img_h * 100, 2) if img_h else 0,
-            "y_max_pct": round(row["y_max"] / img_h * 100, 2) if img_h else 0,
-            "is_header": row["index"] in header_rows,
-        })
-
-    return {
-        "columns": out_columns,
-        "rows": out_rows,
-        "cells": cells,
-        "header_rows": header_rows,
-        "_raw_columns": columns,  # internal: for propagation to other zones
-    }

klausur-service/backend/vocab/__init__.py

@@ -0,0 +1,6 @@
+"""
+Vocab package — restructured from vocab_* flat modules.
+
+Backward-compatible re-exports: consumers can still use
+``from vocab_worksheet_api import ...`` etc. via the shim files in backend/.
+"""

klausur-service/backend/vocab/learn_bridge.py

@@ -0,0 +1,196 @@
+"""
+Vocab Learn Bridge — Converts vocabulary session data into Learning Units.
+
+Bridges klausur-service (vocab extraction) with backend-lehrer (learning units + generators).
+Creates a Learning Unit in backend-lehrer, then triggers MC/Cloze/QA generation.
+
+DATENSCHUTZ: All communication stays within Docker network (breakpilot-network).
+"""
+
+import os
+import json
+import logging
+import httpx
+from typing import List, Dict, Any, Optional
+
+logger = logging.getLogger(__name__)
+
+BACKEND_LEHRER_URL = os.getenv("BACKEND_LEHRER_URL", "http://backend-lehrer:8001")
+
+
+def vocab_to_analysis_data(session_name: str, vocabulary: List[Dict[str, Any]]) -> Dict[str, Any]:
+    """
+    Convert vocabulary entries from a vocab session into the analysis_data format
+    expected by backend-lehrer generators (MC, Cloze, QA).
+
+    The generators consume:
+    - title: Display name
+    - subject: Subject area
+    - grade_level: Target grade
+    - canonical_text: Full text representation
+    - printed_blocks: Individual text blocks
+    - vocabulary: Original vocab data (for vocab-specific modules)
+    """
+    canonical_lines = []
+    printed_blocks = []
+
+    for v in vocabulary:
+        en = v.get("english", "").strip()
+        de = v.get("german", "").strip()
+        example = v.get("example_sentence", "").strip()
+
+        if not en and not de:
+            continue
+
+        line = f"{en} = {de}"
+        if example:
+            line += f" ({example})"
+        canonical_lines.append(line)
+
+        block_text = f"{en} — {de}"
+        if example:
+            block_text += f" | {example}"
+        printed_blocks.append({"text": block_text})
+
+    return {
+        "title": session_name,
+        "subject": "English Vocabulary",
+        "grade_level": "5-8",
+        "canonical_text": "\n".join(canonical_lines),
+        "printed_blocks": printed_blocks,
+        "vocabulary": vocabulary,
+    }
+
+
+async def create_learning_unit(
+    session_name: str,
+    vocabulary: List[Dict[str, Any]],
+    grade: Optional[str] = None,
+) -> Dict[str, Any]:
+    """
+    Create a Learning Unit in backend-lehrer from vocabulary data.
+
+    Steps:
+    1. Create unit via POST /api/learning-units/
+    2. Return the created unit info
+
+    Returns dict with unit_id, status, vocabulary_count.
+    """
+    if not vocabulary:
+        raise ValueError("No vocabulary entries provided")
+
+    analysis_data = vocab_to_analysis_data(session_name, vocabulary)
+
+    async with httpx.AsyncClient(timeout=30.0) as client:
+        # 1. Create Learning Unit
+        create_payload = {
+            "title": session_name,
+            "subject": "Englisch",
+            "grade": grade or "5-8",
+        }
+
+        try:
+            resp = await client.post(
+                f"{BACKEND_LEHRER_URL}/api/learning-units/",
+                json=create_payload,
+            )
+            resp.raise_for_status()
+            unit = resp.json()
+        except httpx.HTTPError as e:
+            logger.error(f"Failed to create learning unit: {e}")
+            raise RuntimeError(f"Backend-Lehrer nicht erreichbar: {e}")
+
+        unit_id = unit.get("id")
+        if not unit_id:
+            raise RuntimeError("Learning Unit created but no ID returned")
+
+        logger.info(f"Created learning unit {unit_id} with {len(vocabulary)} vocabulary entries")
+
+        # 2. Save analysis_data as JSON file for generators
+        analysis_dir = os.path.expanduser("~/Arbeitsblaetter/Lerneinheiten")
+        os.makedirs(analysis_dir, exist_ok=True)
+        analysis_path = os.path.join(analysis_dir, f"{unit_id}_analyse.json")
+
+        with open(analysis_path, "w", encoding="utf-8") as f:
+            json.dump(analysis_data, f, ensure_ascii=False, indent=2)
+
+        logger.info(f"Saved analysis data to {analysis_path}")
+
+        return {
+            "unit_id": unit_id,
+            "unit": unit,
+            "analysis_path": analysis_path,
+            "vocabulary_count": len(vocabulary),
+            "status": "created",
+        }
+
+
+async def generate_learning_modules(
+    unit_id: str,
+    analysis_path: str,
+) -> Dict[str, Any]:
+    """
+    Trigger MC, Cloze, and QA generation from analysis data.
+
+    Imports generators directly (they run in-process for klausur-service)
+    or calls backend-lehrer API if generators aren't available locally.
+
+    Returns dict with generation results.
+    """
+    results = {
+        "unit_id": unit_id,
+        "mc": {"status": "pending"},
+        "cloze": {"status": "pending"},
+        "qa": {"status": "pending"},
+    }
+
+    # Load analysis data
+    with open(analysis_path, "r", encoding="utf-8") as f:
+        analysis_data = json.load(f)
+
+    # Try to generate via backend-lehrer API
+    async with httpx.AsyncClient(timeout=120.0) as client:
+        # Generate QA (includes Leitner fields)
+        try:
+            resp = await client.post(
+                f"{BACKEND_LEHRER_URL}/api/learning-units/{unit_id}/generate-qa",
+                json={"analysis_data": analysis_data, "num_questions": min(len(analysis_data.get("vocabulary", [])), 20)},
+            )
+            if resp.status_code == 200:
+                results["qa"] = {"status": "generated", "data": resp.json()}
+            else:
+                logger.warning(f"QA generation returned {resp.status_code}")
+                results["qa"] = {"status": "skipped", "reason": f"HTTP {resp.status_code}"}
+        except Exception as e:
+            logger.warning(f"QA generation failed: {e}")
+            results["qa"] = {"status": "error", "reason": str(e)}
+
+        # Generate MC
+        try:
+            resp = await client.post(
+                f"{BACKEND_LEHRER_URL}/api/learning-units/{unit_id}/generate-mc",
+                json={"analysis_data": analysis_data, "num_questions": min(len(analysis_data.get("vocabulary", [])), 10)},
+            )
+            if resp.status_code == 200:
+                results["mc"] = {"status": "generated", "data": resp.json()}
+            else:
+                results["mc"] = {"status": "skipped", "reason": f"HTTP {resp.status_code}"}
+        except Exception as e:
+            logger.warning(f"MC generation failed: {e}")
+            results["mc"] = {"status": "error", "reason": str(e)}
+
+        # Generate Cloze
+        try:
+            resp = await client.post(
+                f"{BACKEND_LEHRER_URL}/api/learning-units/{unit_id}/generate-cloze",
+                json={"analysis_data": analysis_data},
+            )
+            if resp.status_code == 200:
+                results["cloze"] = {"status": "generated", "data": resp.json()}
+            else:
+                results["cloze"] = {"status": "skipped", "reason": f"HTTP {resp.status_code}"}
+        except Exception as e:
+            logger.warning(f"Cloze generation failed: {e}")
+            results["cloze"] = {"status": "error", "reason": str(e)}
+
+    return results

klausur-service/backend/vocab/session_store.py

@@ -0,0 +1,427 @@
+"""
+Vocabulary Session Store - PostgreSQL persistence for vocab extraction sessions.
+
+Replaces in-memory storage with database persistence.
+See migrations/001_vocab_sessions.sql for schema.
+"""
+
+import os
+import uuid
+import logging
+import json
+from typing import Optional, List, Dict, Any
+
+import asyncpg
+
+logger = logging.getLogger(__name__)
+
+# Database configuration
+DATABASE_URL = os.getenv(
+    "DATABASE_URL",
+    "postgresql://breakpilot:breakpilot@postgres:5432/breakpilot_db"
+)
+
+# Connection pool (initialized lazily)
+_pool: Optional[asyncpg.Pool] = None
+
+
+async def get_pool() -> asyncpg.Pool:
+    """Get or create the database connection pool."""
+    global _pool
+    if _pool is None:
+        _pool = await asyncpg.create_pool(DATABASE_URL, min_size=2, max_size=10)
+    return _pool
+
+
+async def init_vocab_tables():
+    """
+    Initialize vocab tables if they don't exist.
+    This is called at startup.
+    """
+    pool = await get_pool()
+    async with pool.acquire() as conn:
+        # Check if tables exist
+        tables_exist = await conn.fetchval("""
+            SELECT EXISTS (
+                SELECT FROM information_schema.tables
+                WHERE table_name = 'vocab_sessions'
+            )
+        """)
+
+        if not tables_exist:
+            logger.info("Creating vocab tables...")
+            # Read and execute migration
+            migration_path = os.path.join(
+                os.path.dirname(__file__),
+                "migrations/001_vocab_sessions.sql"
+            )
+            if os.path.exists(migration_path):
+                with open(migration_path, "r") as f:
+                    sql = f.read()
+                await conn.execute(sql)
+                logger.info("Vocab tables created successfully")
+            else:
+                logger.warning(f"Migration file not found: {migration_path}")
+        else:
+            logger.debug("Vocab tables already exist")
+
+
+# =============================================================================
+# SESSION OPERATIONS
+# =============================================================================
+
+async def create_session_db(
+    session_id: str,
+    name: str,
+    description: str = "",
+    source_language: str = "en",
+    target_language: str = "de"
+) -> Dict[str, Any]:
+    """Create a new vocabulary session in the database."""
+    pool = await get_pool()
+    async with pool.acquire() as conn:
+        row = await conn.fetchrow("""
+            INSERT INTO vocab_sessions (
+                id, name, description, source_language, target_language,
+                status, vocabulary_count
+            ) VALUES ($1, $2, $3, $4, $5, 'pending', 0)
+            RETURNING *
+        """, uuid.UUID(session_id), name, description, source_language, target_language)
+
+        return _row_to_dict(row)
+
+
+async def get_session_db(session_id: str) -> Optional[Dict[str, Any]]:
+    """Get a session by ID."""
+    pool = await get_pool()
+    async with pool.acquire() as conn:
+        row = await conn.fetchrow("""
+            SELECT * FROM vocab_sessions WHERE id = $1
+        """, uuid.UUID(session_id))
+
+        if row:
+            return _row_to_dict(row)
+        return None
+
+
+async def list_sessions_db(
+    limit: int = 50,
+    offset: int = 0,
+    status: Optional[str] = None
+) -> List[Dict[str, Any]]:
+    """List all sessions with optional filtering."""
+    pool = await get_pool()
+    async with pool.acquire() as conn:
+        if status:
+            rows = await conn.fetch("""
+                SELECT * FROM vocab_sessions
+                WHERE status = $1
+                ORDER BY created_at DESC
+                LIMIT $2 OFFSET $3
+            """, status, limit, offset)
+        else:
+            rows = await conn.fetch("""
+                SELECT * FROM vocab_sessions
+                ORDER BY created_at DESC
+                LIMIT $1 OFFSET $2
+            """, limit, offset)
+
+        return [_row_to_dict(row) for row in rows]
+
+
+async def update_session_db(
+    session_id: str,
+    **kwargs
+) -> Optional[Dict[str, Any]]:
+    """Update a session with given fields."""
+    pool = await get_pool()
+
+    # Build dynamic UPDATE query
+    fields = []
+    values = []
+    param_idx = 1
+
+    allowed_fields = [
+        'name', 'description', 'status', 'vocabulary_count',
+        'extraction_confidence', 'image_path', 'pdf_path', 'pdf_page_count',
+        'ocr_prompts', 'processed_pages', 'successful_pages', 'failed_pages'
+    ]
+
+    for key, value in kwargs.items():
+        if key in allowed_fields:
+            fields.append(f"{key} = ${param_idx}")
+            # Convert dicts/lists to JSON for JSONB columns
+            if key in ['ocr_prompts', 'processed_pages', 'successful_pages', 'failed_pages']:
+                value = json.dumps(value) if value else None
+            values.append(value)
+            param_idx += 1
+
+    if not fields:
+        return await get_session_db(session_id)
+
+    values.append(uuid.UUID(session_id))
+
+    async with pool.acquire() as conn:
+        row = await conn.fetchrow(f"""
+            UPDATE vocab_sessions
+            SET {', '.join(fields)}
+            WHERE id = ${param_idx}
+            RETURNING *
+        """, *values)
+
+        if row:
+            return _row_to_dict(row)
+        return None
+
+
+async def delete_session_db(session_id: str) -> bool:
+    """Delete a session and all related data (cascades)."""
+    pool = await get_pool()
+    async with pool.acquire() as conn:
+        result = await conn.execute("""
+            DELETE FROM vocab_sessions WHERE id = $1
+        """, uuid.UUID(session_id))
+        return result == "DELETE 1"
+
+
+# =============================================================================
+# VOCABULARY OPERATIONS
+# =============================================================================
+
+async def add_vocabulary_db(
+    session_id: str,
+    vocab_list: List[Dict[str, Any]]
+) -> List[Dict[str, Any]]:
+    """Add vocabulary entries to a session."""
+    if not vocab_list:
+        return []
+
+    pool = await get_pool()
+    results = []
+
+    async with pool.acquire() as conn:
+        for vocab in vocab_list:
+            vocab_id = str(uuid.uuid4())
+            row = await conn.fetchrow("""
+                INSERT INTO vocab_entries (
+                    id, session_id, english, german, example_sentence,
+                    example_sentence_gap, word_type, source_page
+                ) VALUES ($1, $2, $3, $4, $5, $6, $7, $8)
+                RETURNING *
+            """,
+                uuid.UUID(vocab_id),
+                uuid.UUID(session_id),
+                vocab.get('english', ''),
+                vocab.get('german', ''),
+                vocab.get('example_sentence'),
+                vocab.get('example_sentence_gap'),
+                vocab.get('word_type'),
+                vocab.get('source_page')
+            )
+            results.append(_row_to_dict(row))
+
+        # Update vocabulary count
+        await conn.execute("""
+            UPDATE vocab_sessions
+            SET vocabulary_count = (
+                SELECT COUNT(*) FROM vocab_entries WHERE session_id = $1
+            )
+            WHERE id = $1
+        """, uuid.UUID(session_id))
+
+    return results
+
+
+async def get_vocabulary_db(
+    session_id: str,
+    source_page: Optional[int] = None
+) -> List[Dict[str, Any]]:
+    """Get vocabulary entries for a session."""
+    pool = await get_pool()
+    async with pool.acquire() as conn:
+        if source_page is not None:
+            rows = await conn.fetch("""
+                SELECT * FROM vocab_entries
+                WHERE session_id = $1 AND source_page = $2
+                ORDER BY created_at
+            """, uuid.UUID(session_id), source_page)
+        else:
+            rows = await conn.fetch("""
+                SELECT * FROM vocab_entries
+                WHERE session_id = $1
+                ORDER BY source_page NULLS LAST, created_at
+            """, uuid.UUID(session_id))
+
+        return [_row_to_dict(row) for row in rows]
+
+
+async def update_vocabulary_db(
+    entry_id: str,
+    **kwargs
+) -> Optional[Dict[str, Any]]:
+    """Update a single vocabulary entry."""
+    pool = await get_pool()
+
+    fields = []
+    values = []
+    param_idx = 1
+
+    allowed_fields = [
+        'english', 'german', 'example_sentence', 'example_sentence_gap',
+        'word_type', 'source_page'
+    ]
+
+    for key, value in kwargs.items():
+        if key in allowed_fields:
+            fields.append(f"{key} = ${param_idx}")
+            values.append(value)
+            param_idx += 1
+
+    if not fields:
+        return None
+
+    values.append(uuid.UUID(entry_id))
+
+    async with pool.acquire() as conn:
+        row = await conn.fetchrow(f"""
+            UPDATE vocab_entries
+            SET {', '.join(fields)}
+            WHERE id = ${param_idx}
+            RETURNING *
+        """, *values)
+
+        if row:
+            return _row_to_dict(row)
+        return None
+
+
+async def clear_page_vocabulary_db(session_id: str, page: int) -> int:
+    """Clear all vocabulary for a specific page."""
+    pool = await get_pool()
+    async with pool.acquire() as conn:
+        result = await conn.execute("""
+            DELETE FROM vocab_entries
+            WHERE session_id = $1 AND source_page = $2
+        """, uuid.UUID(session_id), page)
+
+        # Update vocabulary count
+        await conn.execute("""
+            UPDATE vocab_sessions
+            SET vocabulary_count = (
+                SELECT COUNT(*) FROM vocab_entries WHERE session_id = $1
+            )
+            WHERE id = $1
+        """, uuid.UUID(session_id))
+
+        # Return count of deleted rows
+        count = int(result.split()[-1]) if result else 0
+        return count
+
+
+# =============================================================================
+# WORKSHEET OPERATIONS
+# =============================================================================
+
+async def create_worksheet_db(
+    session_id: str,
+    worksheet_types: List[str],
+    pdf_path: Optional[str] = None,
+    solution_path: Optional[str] = None
+) -> Dict[str, Any]:
+    """Create a worksheet record."""
+    pool = await get_pool()
+    worksheet_id = str(uuid.uuid4())
+
+    async with pool.acquire() as conn:
+        row = await conn.fetchrow("""
+            INSERT INTO vocab_worksheets (
+                id, session_id, worksheet_types, pdf_path, solution_path
+            ) VALUES ($1, $2, $3, $4, $5)
+            RETURNING *
+        """,
+            uuid.UUID(worksheet_id),
+            uuid.UUID(session_id),
+            json.dumps(worksheet_types),
+            pdf_path,
+            solution_path
+        )
+
+        return _row_to_dict(row)
+
+
+async def get_worksheet_db(worksheet_id: str) -> Optional[Dict[str, Any]]:
+    """Get a worksheet by ID."""
+    pool = await get_pool()
+    async with pool.acquire() as conn:
+        row = await conn.fetchrow("""
+            SELECT * FROM vocab_worksheets WHERE id = $1
+        """, uuid.UUID(worksheet_id))
+
+        if row:
+            return _row_to_dict(row)
+        return None
+
+
+async def delete_worksheets_for_session_db(session_id: str) -> int:
+    """Delete all worksheets for a session."""
+    pool = await get_pool()
+    async with pool.acquire() as conn:
+        result = await conn.execute("""
+            DELETE FROM vocab_worksheets WHERE session_id = $1
+        """, uuid.UUID(session_id))
+
+        count = int(result.split()[-1]) if result else 0
+        return count
+
+
+# =============================================================================
+# PDF CACHE OPERATIONS
+# =============================================================================
+
+# Simple in-memory cache for PDF data (temporary until served)
+_pdf_cache: Dict[str, bytes] = {}
+
+
+def cache_pdf_data(worksheet_id: str, pdf_data: bytes) -> None:
+    """Cache PDF data temporarily for download."""
+    _pdf_cache[worksheet_id] = pdf_data
+
+
+def get_cached_pdf_data(worksheet_id: str) -> Optional[bytes]:
+    """Get cached PDF data."""
+    return _pdf_cache.get(worksheet_id)
+
+
+def clear_cached_pdf_data(worksheet_id: str) -> None:
+    """Clear cached PDF data."""
+    _pdf_cache.pop(worksheet_id, None)
+
+
+# =============================================================================
+# HELPER FUNCTIONS
+# =============================================================================
+
+def _row_to_dict(row: asyncpg.Record) -> Dict[str, Any]:
+    """Convert asyncpg Record to dict with proper type handling."""
+    if row is None:
+        return {}
+
+    result = dict(row)
+
+    # Convert UUIDs to strings
+    for key in ['id', 'session_id']:
+        if key in result and result[key] is not None:
+            result[key] = str(result[key])
+
+    # Convert datetimes to ISO strings
+    for key in ['created_at', 'updated_at', 'generated_at']:
+        if key in result and result[key] is not None:
+            result[key] = result[key].isoformat()
+
+    # Parse JSONB fields back to dicts/lists
+    for key in ['ocr_prompts', 'processed_pages', 'successful_pages', 'failed_pages', 'worksheet_types']:
+        if key in result and result[key] is not None:
+            if isinstance(result[key], str):
+                result[key] = json.loads(result[key])
+
+    return result

klausur-service/backend/vocab/worksheet/__init__.py

@@ -0,0 +1,5 @@
+"""
+Vocab worksheet sub-package.
+
+Main entry point: ``from vocab.worksheet.api import router``
+"""

klausur-service/backend/vocab/worksheet/analysis_api.py

@@ -0,0 +1,472 @@
+"""
+Vocabulary Worksheet Analysis API - OCR export, ground truth labeling,
+extract-with-boxes, deskewed images, and learning unit generation.
+
+The two large handlers (compare_ocr_methods, analyze_grid) live in
+vocab_worksheet_compare_api.py and are included via compare_router.
+"""
+
+from fastapi import APIRouter, Body, HTTPException
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+from typing import Optional, Dict, Any
+from datetime import datetime
+import os
+import io
+import json
+import logging
+
+def _get_sessions():
+    from .api import _sessions
+    return _sessions
+
+def _get_local_storage_path():
+    from .api import LOCAL_STORAGE_PATH
+    return LOCAL_STORAGE_PATH
+from .generation import convert_pdf_page_to_image
+
+# Try to import Tesseract extractor
+try:
+    from tesseract_vocab_extractor import (
+        extract_bounding_boxes, TESSERACT_AVAILABLE,
+    )
+except ImportError:
+    TESSERACT_AVAILABLE = False
+
+# Try to import Grid Detection Service
+try:
+    from services.grid_detection_service import GridDetectionService
+    GRID_SERVICE_AVAILABLE = True
+except ImportError:
+    GRID_SERVICE_AVAILABLE = False
+
+logger = logging.getLogger(__name__)
+
+analysis_router = APIRouter()
+
+def _ocr_export_dir():
+    return os.path.join(_get_local_storage_path(), "ocr-exports")
+
+def _ground_truth_dir():
+    return os.path.join(_get_local_storage_path(), "ground-truth")
+
+
+# =============================================================================
+# OCR Export Endpoints (for cross-app OCR data sharing)
+# =============================================================================
+
+
+@analysis_router.post("/sessions/{session_id}/ocr-export/{page_number}")
+async def save_ocr_export(session_id: str, page_number: int, data: Dict[str, Any] = Body(...)):
+    """
+    Save OCR export data for cross-app sharing (admin-v2 -> studio-v2).
+
+    Both apps proxy to klausur-service via /klausur-api/, so this endpoint
+    serves as shared storage accessible from both ports.
+    """
+
+    logger.info(f"Saving OCR export for session {session_id}, page {page_number}")
+
+    os.makedirs(_ocr_export_dir(), exist_ok=True)
+
+    # Save the export data
+    export_path = os.path.join(_ocr_export_dir(), f"{session_id}_page{page_number}.json")
+    with open(export_path, 'w', encoding='utf-8') as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+
+    # Update latest pointer
+    latest_path = os.path.join(_ocr_export_dir(), "latest.json")
+    with open(latest_path, 'w', encoding='utf-8') as f:
+        json.dump({
+            "session_id": session_id,
+            "page_number": page_number,
+            "saved_at": datetime.utcnow().isoformat(),
+        }, f, ensure_ascii=False, indent=2)
+
+    return {
+        "success": True,
+        "session_id": session_id,
+        "page_number": page_number,
+        "message": "OCR export saved successfully",
+    }
+
+
+@analysis_router.get("/sessions/{session_id}/ocr-export/{page_number}")
+async def load_ocr_export(session_id: str, page_number: int):
+    """Load a specific OCR export by session and page number."""
+
+    export_path = os.path.join(_ocr_export_dir(), f"{session_id}_page{page_number}.json")
+
+    if not os.path.exists(export_path):
+        raise HTTPException(status_code=404, detail="OCR export not found")
+
+    with open(export_path, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+
+    return data
+
+
+@analysis_router.get("/ocr-export/latest")
+async def load_latest_ocr_export():
+    """Load the most recently saved OCR export data."""
+
+    latest_path = os.path.join(_ocr_export_dir(), "latest.json")
+
+    if not os.path.exists(latest_path):
+        raise HTTPException(status_code=404, detail="No OCR exports found")
+
+    with open(latest_path, 'r', encoding='utf-8') as f:
+        pointer = json.load(f)
+
+    session_id = pointer.get("session_id")
+    page_number = pointer.get("page_number")
+
+    export_path = os.path.join(_ocr_export_dir(), f"{session_id}_page{page_number}.json")
+
+    if not os.path.exists(export_path):
+        raise HTTPException(status_code=404, detail="Latest OCR export file not found")
+
+    with open(export_path, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+
+    return data
+
+
+# =============================================================================
+# Extract with Boxes & Deskewed Image
+# =============================================================================
+
+
+async def extract_entries_with_boxes(image_bytes: bytes, lang: str = "eng+deu") -> dict:
+    """Extract vocabulary entries with bounding boxes using Tesseract + GridDetectionService.
+
+    Returns dict with 'entries' list and 'image_width'/'image_height'.
+    Each entry has row_index, english, german, example, confidence, bbox, bbox_en, bbox_de, bbox_ex.
+    All bbox coordinates are in percent (0-100).
+    """
+    if not TESSERACT_AVAILABLE:
+        raise HTTPException(status_code=500, detail="Tesseract not available")
+    if not GRID_SERVICE_AVAILABLE:
+        raise HTTPException(status_code=500, detail="GridDetectionService not available")
+
+    # Step 1: Tesseract word-level bounding boxes
+    tess_result = await extract_bounding_boxes(image_bytes, lang=lang)
+    words = tess_result.get("words", [])
+    img_w = tess_result.get("image_width", 0)
+    img_h = tess_result.get("image_height", 0)
+
+    if not words or img_w == 0 or img_h == 0:
+        return {"entries": [], "image_width": img_w, "image_height": img_h}
+
+    # Step 2: Convert to OCR regions (percentage-based)
+    service = GridDetectionService()
+    regions = service.convert_tesseract_regions(words, img_w, img_h)
+
+    if not regions:
+        return {"entries": [], "image_width": img_w, "image_height": img_h}
+
+    # Step 3: Detect grid
+    grid_result = service.detect_grid(regions)
+
+    if not grid_result.cells:
+        return {"entries": [], "image_width": img_w, "image_height": img_h}
+
+    # Step 4: Group cells by logical_row and column_type
+    from services.grid_detection_service import ColumnType
+
+    entries = []
+    for row_idx, row_cells in enumerate(grid_result.cells):
+        en_text = ""
+        de_text = ""
+        ex_text = ""
+        en_bbox = None
+        de_bbox = None
+        ex_bbox = None
+        row_conf_sum = 0.0
+        row_conf_count = 0
+
+        for cell in row_cells:
+            cell_bbox = {"x": round(cell.x, 2), "y": round(cell.y, 2),
+                         "w": round(cell.width, 2), "h": round(cell.height, 2)}
+
+            if cell.column_type == ColumnType.ENGLISH:
+                en_text = cell.text.strip()
+                en_bbox = cell_bbox
+            elif cell.column_type == ColumnType.GERMAN:
+                de_text = cell.text.strip()
+                de_bbox = cell_bbox
+            elif cell.column_type == ColumnType.EXAMPLE:
+                ex_text = cell.text.strip()
+                ex_bbox = cell_bbox
+
+            if cell.text.strip():
+                row_conf_sum += cell.confidence
+                row_conf_count += 1
+
+        # Skip completely empty rows
+        if not en_text and not de_text and not ex_text:
+            continue
+
+        # Calculate whole-row bounding box
+        all_bboxes = [b for b in [en_bbox, de_bbox, ex_bbox] if b is not None]
+        if all_bboxes:
+            row_x = min(b["x"] for b in all_bboxes)
+            row_y = min(b["y"] for b in all_bboxes)
+            row_right = max(b["x"] + b["w"] for b in all_bboxes)
+            row_bottom = max(b["y"] + b["h"] for b in all_bboxes)
+            row_bbox = {"x": round(row_x, 2), "y": round(row_y, 2),
+                        "w": round(row_right - row_x, 2), "h": round(row_bottom - row_y, 2)}
+        else:
+            row_bbox = {"x": 0, "y": 0, "w": 100, "h": 3}
+
+        avg_conf = round((row_conf_sum / row_conf_count * 100) if row_conf_count > 0 else 0, 1)
+
+        entries.append({
+            "row_index": row_idx,
+            "english": en_text,
+            "german": de_text,
+            "example": ex_text,
+            "confidence": avg_conf,
+            "bbox": row_bbox,
+            "bbox_en": en_bbox or {"x": 0, "y": 0, "w": 0, "h": 0},
+            "bbox_de": de_bbox or {"x": 0, "y": 0, "w": 0, "h": 0},
+            "bbox_ex": ex_bbox or {"x": 0, "y": 0, "w": 0, "h": 0},
+        })
+
+    return {"entries": entries, "image_width": img_w, "image_height": img_h}
+
+
+@analysis_router.post("/sessions/{session_id}/extract-with-boxes/{page_number}")
+async def extract_with_boxes(session_id: str, page_number: int):
+    """Extract vocabulary entries with bounding boxes for ground truth labeling.
+
+    Uses Tesseract + GridDetectionService for spatial positioning.
+    page_number is 0-indexed.
+    """
+    logger.info(f"Extract with boxes for session {session_id}, page {page_number}")
+
+    if session_id not in _get_sessions():
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    session = _get_sessions()[session_id]
+    pdf_data = session.get("pdf_data")
+
+    if not pdf_data:
+        raise HTTPException(status_code=400, detail="No PDF uploaded for this session")
+
+    page_count = session.get("pdf_page_count", 1)
+    if page_number < 0 or page_number >= page_count:
+        raise HTTPException(status_code=400, detail=f"Invalid page number. PDF has {page_count} pages (0-indexed).")
+
+    # Convert page to hires image
+    image_data = await convert_pdf_page_to_image(pdf_data, page_number, thumbnail=False)
+
+    # Deskew image before OCR
+    deskew_angle = 0.0
+    try:
+        from cv_vocab_pipeline import deskew_image_by_word_alignment, CV2_AVAILABLE
+        if CV2_AVAILABLE:
+            image_data, deskew_angle = deskew_image_by_word_alignment(image_data)
+            logger.info(f"Deskew: {deskew_angle:.2f}° for page {page_number}")
+    except Exception as e:
+        logger.warning(f"Deskew failed for page {page_number}: {e}")
+
+    # Cache deskewed image in session for later serving
+    if "deskewed_images" not in session:
+        session["deskewed_images"] = {}
+    session["deskewed_images"][str(page_number)] = image_data
+
+    # Extract entries with boxes (now on deskewed image)
+    result = await extract_entries_with_boxes(image_data)
+
+    # Cache in session
+    if "gt_entries" not in session:
+        session["gt_entries"] = {}
+    session["gt_entries"][str(page_number)] = result["entries"]
+
+    return {
+        "success": True,
+        "entries": result["entries"],
+        "entry_count": len(result["entries"]),
+        "image_width": result["image_width"],
+        "image_height": result["image_height"],
+        "deskew_angle": round(deskew_angle, 2),
+        "deskewed": abs(deskew_angle) > 0.05,
+    }
+
+
+@analysis_router.get("/sessions/{session_id}/deskewed-image/{page_number}")
+async def get_deskewed_image(session_id: str, page_number: int):
+    """Return the deskewed page image as PNG.
+
+    Falls back to the original hires image if no deskewed version is cached.
+    """
+    if session_id not in _get_sessions():
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    session = _get_sessions()[session_id]
+    deskewed = session.get("deskewed_images", {}).get(str(page_number))
+
+    if deskewed:
+        return StreamingResponse(io.BytesIO(deskewed), media_type="image/png")
+
+    # Fallback: render original hires image
+    pdf_data = session.get("pdf_data")
+    if not pdf_data:
+        raise HTTPException(status_code=400, detail="No PDF uploaded for this session")
+
+    image_data = await convert_pdf_page_to_image(pdf_data, page_number, thumbnail=False)
+    return StreamingResponse(io.BytesIO(image_data), media_type="image/png")
+
+
+# =============================================================================
+# Ground Truth Labeling
+# =============================================================================
+
+
+@analysis_router.post("/sessions/{session_id}/ground-truth/{page_number}")
+async def save_ground_truth(session_id: str, page_number: int, data: dict = Body(...)):
+    """Save ground truth labels for a page.
+
+    Expects body with 'entries' list - each entry has english, german, example,
+    status ('confirmed' | 'edited' | 'skipped'), and bbox fields.
+    """
+    logger.info(f"Save ground truth for session {session_id}, page {page_number}")
+
+    if session_id not in _get_sessions():
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    entries = data.get("entries", [])
+    if not entries:
+        raise HTTPException(status_code=400, detail="No entries provided")
+
+    # Save in session
+    session = _get_sessions()[session_id]
+    if "ground_truth" not in session:
+        session["ground_truth"] = {}
+    session["ground_truth"][str(page_number)] = entries
+
+    # Also save to disk
+    os.makedirs(_ground_truth_dir(), exist_ok=True)
+    gt_path = os.path.join(_ground_truth_dir(), f"{session_id}_page{page_number}.json")
+    gt_data = {
+        "session_id": session_id,
+        "page_number": page_number,
+        "saved_at": datetime.now().isoformat(),
+        "entry_count": len(entries),
+        "entries": entries,
+    }
+    with open(gt_path, 'w', encoding='utf-8') as f:
+        json.dump(gt_data, f, ensure_ascii=False, indent=2)
+
+    logger.info(f"Ground truth saved: {len(entries)} entries to {gt_path}")
+
+    confirmed = sum(1 for e in entries if e.get("status") == "confirmed")
+    edited = sum(1 for e in entries if e.get("status") == "edited")
+    skipped = sum(1 for e in entries if e.get("status") == "skipped")
+
+    return {
+        "success": True,
+        "saved_count": len(entries),
+        "confirmed": confirmed,
+        "edited": edited,
+        "skipped": skipped,
+        "file_path": gt_path,
+    }
+
+
+@analysis_router.get("/sessions/{session_id}/ground-truth/{page_number}")
+async def load_ground_truth(session_id: str, page_number: int):
+    """Load saved ground truth for a page."""
+    logger.info(f"Load ground truth for session {session_id}, page {page_number}")
+
+    if session_id not in _get_sessions():
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    # Try session cache first
+    session = _get_sessions()[session_id]
+    cached = session.get("ground_truth", {}).get(str(page_number))
+    if cached:
+        return {"success": True, "entries": cached, "source": "cache"}
+
+    # Try disk
+    gt_path = os.path.join(_ground_truth_dir(), f"{session_id}_page{page_number}.json")
+    if not os.path.exists(gt_path):
+        raise HTTPException(status_code=404, detail="No ground truth found for this page")
+
+    with open(gt_path, 'r', encoding='utf-8') as f:
+        gt_data = json.load(f)
+
+    return {"success": True, "entries": gt_data.get("entries", []), "source": "disk"}
+
+
+# ─── Learning Module Generation ─────────────────────────────────────────────
+
+
+class GenerateLearningUnitRequest(BaseModel):
+    grade: Optional[str] = None
+    generate_modules: bool = True
+
+
+@analysis_router.post("/sessions/{session_id}/generate-learning-unit")
+async def generate_learning_unit_endpoint(session_id: str, request: GenerateLearningUnitRequest = None):
+    """
+    Create a Learning Unit from the vocabulary in this session.
+
+    1. Takes vocabulary from the session
+    2. Creates a Learning Unit in backend-lehrer
+    3. Optionally triggers MC/Cloze/QA generation
+
+    Returns the created unit info and generation status.
+    """
+    if request is None:
+        request = GenerateLearningUnitRequest()
+
+    if session_id not in _get_sessions():
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    session = _get_sessions()[session_id]
+    vocabulary = session.get("vocabulary", [])
+
+    if not vocabulary:
+        raise HTTPException(status_code=400, detail="No vocabulary in this session")
+
+    try:
+        from vocab.learn_bridge import create_learning_unit, generate_learning_modules
+
+        # Step 1: Create Learning Unit
+        result = await create_learning_unit(
+            session_name=session["name"],
+            vocabulary=vocabulary,
+            grade=request.grade,
+        )
+
+        # Step 2: Generate modules if requested
+        if request.generate_modules:
+            try:
+                gen_result = await generate_learning_modules(
+                    unit_id=result["unit_id"],
+                    analysis_path=result["analysis_path"],
+                )
+                result["generation"] = gen_result
+            except Exception as e:
+                logger.warning(f"Module generation failed (unit created): {e}")
+                result["generation"] = {"status": "error", "reason": str(e)}
+
+        return result
+
+    except ImportError:
+        raise HTTPException(status_code=501, detail="vocab_learn_bridge module not available")
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    except RuntimeError as e:
+        raise HTTPException(status_code=502, detail=str(e))
+
+
+# =============================================================================
+# Include compare_ocr_methods & analyze_grid from companion module
+# =============================================================================
+
+from .compare_api import compare_router  # noqa: E402
+
+analysis_router.include_router(compare_router)

klausur-service/backend/vocab/worksheet/api.py

@@ -0,0 +1,498 @@
+"""
+Vocabulary Worksheet API — core CRUD routes for sessions, uploads,
+vocabulary editing, worksheet generation, and PDF downloads.
+
+Sub-routers (included at bottom):
+- vocab_worksheet_upload_api: PDF upload, thumbnails, page processing
+- vocab_worksheet_analysis_api: OCR compare, grid analysis, ground truth
+"""
+
+from fastapi import APIRouter, HTTPException, UploadFile, File, Query
+from fastapi.responses import StreamingResponse
+from typing import List, Dict, Any
+from datetime import datetime
+import uuid
+import os
+import io
+import logging
+
+logger = logging.getLogger(__name__)
+
+# --- Imports from extracted sub-modules ---
+from .models import (
+    SessionStatus,
+    VocabularyEntry,
+    SessionCreate,
+    SessionResponse,
+    VocabularyResponse,
+    VocabularyUpdate,
+    WorksheetGenerateRequest,
+    WorksheetResponse,
+)
+from .extraction import extract_vocabulary_from_image
+from .generation import (
+    generate_worksheet_html, generate_worksheet_pdf,
+    convert_pdf_page_to_image,
+)
+
+# --- Database integration (used by main.py lifespan) ---
+try:
+    from vocab.session_store import (
+        DATABASE_URL, get_pool, init_vocab_tables,
+        list_sessions_db, get_session_db,
+    )
+except ImportError:
+    DATABASE_URL = os.getenv("DATABASE_URL", "postgresql://breakpilot:breakpilot@postgres:5432/breakpilot_db")
+    get_pool = None
+    init_vocab_tables = None
+    list_sessions_db = None
+    get_session_db = None
+
+_db_pool = None
+
+
+def set_db_pool(pool):
+    """Set the database connection pool (called from main.py lifespan)."""
+    global _db_pool
+    _db_pool = pool
+
+
+async def _init_vocab_table():
+    """Initialize vocab tables in database."""
+    if init_vocab_tables:
+        try:
+            await init_vocab_tables()
+            logger.info("vocab_session_cache table ready")
+        except Exception as e:
+            logger.warning(f"Failed to init vocab tables: {e}")
+    else:
+        logger.info("vocab_session_cache table ready")
+
+
+async def _load_all_sessions():
+    """Load all vocab sessions from database into memory cache."""
+    if not list_sessions_db:
+        logger.info("Loaded 0 vocab sessions from database")
+        return
+
+    try:
+        sessions = await list_sessions_db(limit=500)
+        count = 0
+        for s in sessions:
+            sid = s.get("id") or s.get("session_id")
+            if sid and sid not in _sessions:
+                _sessions[sid] = {
+                    "id": sid,
+                    "name": s.get("name", ""),
+                    "description": s.get("description", ""),
+                    "status": s.get("status", "created"),
+                    "vocabulary_count": s.get("vocabulary_count", 0),
+                    "source_language": s.get("source_language", "en"),
+                    "target_language": s.get("target_language", "de"),
+                    "created_at": str(s.get("created_at", "")),
+                }
+                count += 1
+        logger.info(f"Loaded {count} vocab sessions from database")
+    except Exception as e:
+        logger.warning(f"Failed to load sessions from database: {e}")
+
+
+# --- Router & module-level state ---
+router = APIRouter(prefix="/api/v1/vocab", tags=["Vocabulary Worksheets"])
+LOCAL_STORAGE_PATH = os.getenv("VOCAB_STORAGE_PATH", "/app/vocab-worksheets")
+_sessions: Dict[str, Dict[str, Any]] = {}
+_worksheets: Dict[str, Dict[str, Any]] = {}
+
+
+@router.post("/sessions", response_model=SessionResponse)
+async def create_session(session: SessionCreate):
+    """Create a new vocabulary extraction session."""
+    session_id = str(uuid.uuid4())
+
+    session_data = {
+        "id": session_id,
+        "name": session.name,
+        "description": session.description,
+        "source_language": session.source_language,
+        "target_language": session.target_language,
+        "status": SessionStatus.PENDING.value,
+        "vocabulary": [],
+        "vocabulary_count": 0,
+        "image_path": None,
+        "extraction_confidence": None,
+        "created_at": datetime.utcnow(),
+    }
+
+    _sessions[session_id] = session_data
+
+    # Create storage directory
+    session_dir = os.path.join(LOCAL_STORAGE_PATH, session_id)
+    os.makedirs(session_dir, exist_ok=True)
+
+    return SessionResponse(
+        id=session_id,
+        name=session.name,
+        description=session.description,
+        source_language=session.source_language,
+        target_language=session.target_language,
+        status=SessionStatus.PENDING.value,
+        vocabulary_count=0,
+        image_path=None,
+        created_at=session_data["created_at"],
+    )
+
+
+@router.get("/sessions", response_model=List[SessionResponse])
+async def list_sessions(limit: int = Query(50, ge=1, le=100)):
+    """List all vocabulary sessions."""
+    sessions = sorted(
+        _sessions.values(),
+        key=lambda x: x["created_at"],
+        reverse=True
+    )[:limit]
+
+    return [
+        SessionResponse(
+            id=s["id"],
+            name=s["name"],
+            description=s.get("description"),
+            source_language=s["source_language"],
+            target_language=s["target_language"],
+            status=s["status"],
+            vocabulary_count=s.get("vocabulary_count", 0),
+            image_path=s.get("image_path"),
+            created_at=s["created_at"],
+        )
+        for s in sessions
+    ]
+
+
+@router.get("/sessions/{session_id}", response_model=SessionResponse)
+async def get_session(session_id: str):
+    """Get a specific session."""
+    if session_id not in _sessions:
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    s = _sessions[session_id]
+    return SessionResponse(
+        id=s["id"],
+        name=s["name"],
+        description=s.get("description"),
+        source_language=s["source_language"],
+        target_language=s["target_language"],
+        status=s["status"],
+        vocabulary_count=s.get("vocabulary_count", 0),
+        image_path=s.get("image_path"),
+        created_at=s["created_at"],
+    )
+
+
+@router.post("/sessions/{session_id}/upload")
+async def upload_image(
+    session_id: str,
+    file: UploadFile = File(...),
+):
+    """
+    Upload a textbook page image or PDF and extract vocabulary.
+
+    Supported formats: PNG, JPG, JPEG, PDF
+    """
+    logger.info(f"Upload request for session {session_id}")
+    logger.info(f"File: filename={file.filename}, content_type={file.content_type}")
+
+    if session_id not in _sessions:
+        logger.error(f"Session {session_id} not found")
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    session = _sessions[session_id]
+
+    # Validate file type - check both extension and content type
+    extension = file.filename.split('.')[-1].lower() if file.filename else ''
+    content_type = file.content_type or ''
+
+    # Accept images and PDFs
+    valid_image_extensions = ['png', 'jpg', 'jpeg']
+    valid_image_content_types = ['image/png', 'image/jpeg', 'image/jpg']
+    is_pdf = extension == 'pdf' or content_type == 'application/pdf'
+    is_image = extension in valid_image_extensions or content_type in valid_image_content_types
+
+    if not is_pdf and not is_image:
+        logger.error(f"Invalid file type: extension={extension}, content_type={content_type}")
+        raise HTTPException(
+            status_code=400,
+            detail=f"Only PNG, JPG, JPEG, PDF files are supported. Got: extension={extension}, content_type={content_type}"
+        )
+
+    # Determine final extension for saving
+    if is_pdf:
+        save_extension = 'png'  # PDFs will be converted to PNG
+    elif extension in valid_image_extensions:
+        save_extension = extension
+    elif content_type == 'image/png':
+        save_extension = 'png'
+    else:
+        save_extension = 'jpg'
+
+    # Read file content
+    content = await file.read()
+    logger.info(f"Read {len(content)} bytes from uploaded file")
+
+    # Convert PDF to image if needed
+    if is_pdf:
+        logger.info("Converting PDF to image...")
+        content = await convert_pdf_page_to_image(content, page_number=0)
+        logger.info(f"PDF converted, image size: {len(content)} bytes")
+
+    # Save image
+    session_dir = os.path.join(LOCAL_STORAGE_PATH, session_id)
+    os.makedirs(session_dir, exist_ok=True)
+    image_path = os.path.join(session_dir, f"source.{save_extension}")
+
+    with open(image_path, 'wb') as f:
+        f.write(content)
+
+    # Update session status
+    session["status"] = SessionStatus.PROCESSING.value
+    session["image_path"] = image_path
+
+    # Extract vocabulary using Vision LLM
+    vocabulary, confidence, error = await extract_vocabulary_from_image(content, file.filename or "image.png", page_number=0)
+
+    # Update session with extracted vocabulary
+    session["vocabulary"] = [v.dict() for v in vocabulary]
+    session["vocabulary_count"] = len(vocabulary)
+    session["extraction_confidence"] = confidence
+    session["status"] = SessionStatus.EXTRACTED.value
+
+    result = {
+        "session_id": session_id,
+        "filename": file.filename,
+        "image_path": image_path,
+        "vocabulary_count": len(vocabulary),
+        "extraction_confidence": confidence,
+        "status": SessionStatus.EXTRACTED.value,
+    }
+
+    if error:
+        result["error"] = error
+
+    return result
+
+
+@router.get("/sessions/{session_id}/vocabulary", response_model=VocabularyResponse)
+async def get_vocabulary(session_id: str):
+    """Get extracted vocabulary for a session."""
+    if session_id not in _sessions:
+        raise HTTPException(status_code=404, detail="Session not found")
+    session = _sessions[session_id]
+    vocabulary = [VocabularyEntry(**v) for v in session.get("vocabulary", [])]
+    return VocabularyResponse(
+        session_id=session_id,
+        vocabulary=vocabulary,
+        extraction_confidence=session.get("extraction_confidence"),
+    )
+
+
+@router.put("/sessions/{session_id}/vocabulary")
+async def update_vocabulary(session_id: str, update: VocabularyUpdate):
+    """Update vocabulary entries (for manual corrections)."""
+    if session_id not in _sessions:
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    session = _sessions[session_id]
+    session["vocabulary"] = [v.dict() for v in update.vocabulary]
+    session["vocabulary_count"] = len(update.vocabulary)
+
+    return {
+        "session_id": session_id,
+        "vocabulary_count": len(update.vocabulary),
+        "message": "Vocabulary updated successfully",
+    }
+
+
+@router.post("/sessions/{session_id}/generate", response_model=WorksheetResponse)
+async def generate_worksheet(session_id: str, request: WorksheetGenerateRequest):
+    """Generate worksheet PDF(s) from extracted vocabulary."""
+    if session_id not in _sessions:
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    session = _sessions[session_id]
+    vocabulary = [VocabularyEntry(**v) for v in session.get("vocabulary", [])]
+
+    if not vocabulary:
+        raise HTTPException(status_code=400, detail="No vocabulary to generate worksheet from")
+
+    worksheet_id = str(uuid.uuid4())
+    title = request.title or session["name"]
+
+    # Generate HTML for each worksheet type
+    combined_html = ""
+    for wtype in request.worksheet_types:
+        html = generate_worksheet_html(
+            vocabulary=vocabulary,
+            worksheet_type=wtype,
+            title=f"{title} - {wtype.value}",
+            show_solutions=False,
+            repetitions=request.repetitions,
+            line_height=request.line_height,
+        )
+        combined_html += html + '<div style="page-break-after: always;"></div>'
+
+    # Generate PDF
+    try:
+        pdf_bytes = await generate_worksheet_pdf(combined_html)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"PDF generation failed: {e}")
+
+    # Save PDF
+    session_dir = os.path.join(LOCAL_STORAGE_PATH, session_id)
+    pdf_path = os.path.join(session_dir, f"worksheet_{worksheet_id}.pdf")
+    with open(pdf_path, 'wb') as f:
+        f.write(pdf_bytes)
+
+    # Generate solution PDF if requested
+    solution_path = None
+    if request.include_solutions:
+        solution_html = ""
+        for wtype in request.worksheet_types:
+            html = generate_worksheet_html(
+                vocabulary=vocabulary,
+                worksheet_type=wtype,
+                title=f"{title} - {wtype.value} (Loesung)",
+                show_solutions=True,
+                repetitions=request.repetitions,
+                line_height=request.line_height,
+            )
+            solution_html += html + '<div style="page-break-after: always;"></div>'
+
+        solution_bytes = await generate_worksheet_pdf(solution_html)
+        solution_path = os.path.join(session_dir, f"solution_{worksheet_id}.pdf")
+        with open(solution_path, 'wb') as f:
+            f.write(solution_bytes)
+
+    # Store worksheet info
+    worksheet_data = {
+        "id": worksheet_id,
+        "session_id": session_id,
+        "worksheet_types": [wt.value for wt in request.worksheet_types],
+        "pdf_path": pdf_path,
+        "solution_path": solution_path,
+        "generated_at": datetime.utcnow(),
+    }
+    _worksheets[worksheet_id] = worksheet_data
+
+    # Update session status
+    session["status"] = SessionStatus.COMPLETED.value
+
+    return WorksheetResponse(
+        id=worksheet_id,
+        session_id=session_id,
+        worksheet_types=worksheet_data["worksheet_types"],
+        pdf_path=pdf_path,
+        solution_path=solution_path,
+        generated_at=worksheet_data["generated_at"],
+    )
+
+
+@router.get("/worksheets/{worksheet_id}/pdf")
+async def download_worksheet_pdf(worksheet_id: str):
+    """Download the generated worksheet PDF."""
+    if worksheet_id not in _worksheets:
+        raise HTTPException(status_code=404, detail="Worksheet not found")
+
+    worksheet = _worksheets[worksheet_id]
+    pdf_path = worksheet["pdf_path"]
+
+    if not os.path.exists(pdf_path):
+        raise HTTPException(status_code=404, detail="PDF file not found")
+
+    with open(pdf_path, 'rb') as f:
+        pdf_bytes = f.read()
+
+    return StreamingResponse(
+        io.BytesIO(pdf_bytes),
+        media_type="application/pdf",
+        headers={"Content-Disposition": f"attachment; filename=worksheet_{worksheet_id}.pdf"}
+    )
+
+
+@router.get("/worksheets/{worksheet_id}/solution")
+async def download_solution_pdf(worksheet_id: str):
+    """Download the solution PDF."""
+    if worksheet_id not in _worksheets:
+        raise HTTPException(status_code=404, detail="Worksheet not found")
+
+    worksheet = _worksheets[worksheet_id]
+    solution_path = worksheet.get("solution_path")
+
+    if not solution_path or not os.path.exists(solution_path):
+        raise HTTPException(status_code=404, detail="Solution PDF not found")
+
+    with open(solution_path, 'rb') as f:
+        pdf_bytes = f.read()
+
+    return StreamingResponse(
+        io.BytesIO(pdf_bytes),
+        media_type="application/pdf",
+        headers={"Content-Disposition": f"attachment; filename=solution_{worksheet_id}.pdf"}
+    )
+
+
+@router.get("/sessions/{session_id}/image")
+async def get_session_image(session_id: str):
+    """Get the uploaded source image for a session."""
+    if session_id not in _sessions:
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    session = _sessions[session_id]
+    image_path = session.get("image_path")
+
+    if not image_path or not os.path.exists(image_path):
+        raise HTTPException(status_code=404, detail="Image not found")
+
+    # Determine content type
+    extension = image_path.split('.')[-1].lower()
+    content_type = {
+        'png': 'image/png',
+        'jpg': 'image/jpeg',
+        'jpeg': 'image/jpeg',
+    }.get(extension, 'application/octet-stream')
+
+    with open(image_path, 'rb') as f:
+        image_bytes = f.read()
+
+    return StreamingResponse(
+        io.BytesIO(image_bytes),
+        media_type=content_type,
+    )
+
+
+@router.delete("/sessions/{session_id}")
+async def delete_session(session_id: str):
+    """Delete a vocabulary session and all associated files."""
+    if session_id not in _sessions:
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    # Delete session directory
+    session_dir = os.path.join(LOCAL_STORAGE_PATH, session_id)
+    if os.path.exists(session_dir):
+        import shutil
+        shutil.rmtree(session_dir)
+
+    # Remove from storage
+    del _sessions[session_id]
+
+    # Remove associated worksheets
+    for wid, ws in list(_worksheets.items()):
+        if ws["session_id"] == session_id:
+            del _worksheets[wid]
+
+    return {"message": "Session deleted successfully", "session_id": session_id}
+
+
+# --- Include sub-routers ---
+from .upload_api import upload_router
+from .analysis_api import analysis_router
+
+router.include_router(upload_router)
+router.include_router(analysis_router)

klausur-service/backend/vocab/worksheet/compare_api.py

@@ -0,0 +1,542 @@
+"""
+Vocabulary Worksheet Compare & Grid Analysis API.
+
+Split from vocab_worksheet_analysis_api.py — contains the two largest
+route handlers: compare_ocr_methods (~234 LOC) and analyze_grid (~255 LOC).
+"""
+
+from fastapi import APIRouter, HTTPException, Query
+import base64
+import json
+import logging
+import os
+
+from .extraction import extract_vocabulary_from_image
+
+OLLAMA_URL = os.getenv("OLLAMA_URL", "http://host.docker.internal:11434")
+VISION_MODEL = os.getenv("VISION_MODEL", "llama3.2-vision:11b")
+
+def _get_sessions():
+    from .api import _sessions
+    return _sessions
+from .generation import convert_pdf_page_to_image
+
+# Try to import Tesseract extractor
+try:
+    from tesseract_vocab_extractor import (
+        run_tesseract_pipeline,
+        match_positions_to_vocab, TESSERACT_AVAILABLE,
+    )
+except ImportError:
+    TESSERACT_AVAILABLE = False
+
+# Try to import CV Pipeline
+try:
+    from cv_vocab_pipeline import run_cv_pipeline, CV_PIPELINE_AVAILABLE
+except ImportError:
+    CV_PIPELINE_AVAILABLE = False
+
+# Try to import Grid Detection Service
+try:
+    from services.grid_detection_service import GridDetectionService
+    GRID_SERVICE_AVAILABLE = True
+except ImportError:
+    GRID_SERVICE_AVAILABLE = False
+
+logger = logging.getLogger(__name__)
+
+compare_router = APIRouter()
+
+
+# =============================================================================
+# OCR Compare & Grid Analysis Endpoints
+# =============================================================================
+
+
+@compare_router.post("/sessions/{session_id}/compare-ocr/{page_number}")
+async def compare_ocr_methods(session_id: str, page_number: int):
+    """
+    Run multiple OCR methods on a page and compare results.
+
+    This endpoint:
+    1. Gets the page image from the session's uploaded PDF
+    2. Runs Vision LLM extraction (primary method)
+    3. Optionally runs Tesseract extraction
+    4. Compares found vocabulary across methods
+    5. Returns structured comparison results
+
+    page_number is 0-indexed.
+    """
+    import time
+
+    logger.info(f"Compare OCR for session {session_id}, page {page_number}")
+
+    if session_id not in _get_sessions():
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    session = _get_sessions()[session_id]
+    pdf_data = session.get("pdf_data")
+
+    if not pdf_data:
+        raise HTTPException(status_code=400, detail="No PDF uploaded for this session")
+
+    page_count = session.get("pdf_page_count", 1)
+    if page_number < 0 or page_number >= page_count:
+        raise HTTPException(status_code=400, detail=f"Invalid page number. PDF has {page_count} pages (0-indexed).")
+
+    # Convert page to image
+    image_data = await convert_pdf_page_to_image(pdf_data, page_number, thumbnail=False)
+
+    methods_results = {}
+    all_vocab_sets = {}
+
+    # --- Method: Vision LLM ---
+    try:
+        start = time.time()
+        vocab, confidence, error = await extract_vocabulary_from_image(
+            image_data, f"page_{page_number + 1}.png", page_number=page_number, use_hybrid=False
+        )
+        duration = time.time() - start
+
+        vocab_list = []
+        for v in vocab:
+            entry = v.dict() if hasattr(v, 'dict') else (v.__dict__.copy() if hasattr(v, '__dict__') else dict(v))
+            vocab_list.append({
+                "english": entry.get("english", ""),
+                "german": entry.get("german", ""),
+                "example": entry.get("example_sentence", ""),
+            })
+
+        methods_results["vision_llm"] = {
+            "name": "Vision LLM",
+            "model": VISION_MODEL,
+            "duration_seconds": round(duration, 1),
+            "vocabulary_count": len(vocab_list),
+            "vocabulary": vocab_list,
+            "confidence": confidence,
+            "success": len(vocab_list) > 0 and not error,
+            "error": error if error else None,
+        }
+        all_vocab_sets["vision_llm"] = {(v["english"].lower().strip(), v["german"].lower().strip()) for v in vocab_list if v["english"] and v["german"]}
+    except Exception as e:
+        logger.error(f"Vision LLM failed: {e}")
+        methods_results["vision_llm"] = {
+            "name": "Vision LLM",
+            "model": VISION_MODEL,
+            "duration_seconds": 0,
+            "vocabulary_count": 0,
+            "vocabulary": [],
+            "confidence": 0,
+            "success": False,
+            "error": str(e),
+        }
+        all_vocab_sets["vision_llm"] = set()
+
+    # --- Method: Tesseract OCR (bounding boxes + vocab extraction) ---
+    if TESSERACT_AVAILABLE:
+        try:
+            start = time.time()
+            tess_result = await run_tesseract_pipeline(image_data, lang="eng+deu")
+            duration = time.time() - start
+
+            tess_vocab = tess_result.get("vocabulary", [])
+            tess_words = tess_result.get("words", [])
+
+            # Store Tesseract words in session for later use (grid analysis, position matching)
+            session["tesseract_words"] = tess_words
+            session["tesseract_image_width"] = tess_result.get("image_width", 0)
+            session["tesseract_image_height"] = tess_result.get("image_height", 0)
+            session[f"tesseract_page_{page_number}"] = tess_result
+
+            vocab_list_tess = []
+            for v in tess_vocab:
+                vocab_list_tess.append({
+                    "english": v.get("english", ""),
+                    "german": v.get("german", ""),
+                    "example": v.get("example", ""),
+                })
+
+            methods_results["tesseract"] = {
+                "name": "Tesseract OCR",
+                "model": "tesseract-ocr (eng+deu)",
+                "duration_seconds": round(duration, 1),
+                "vocabulary_count": len(vocab_list_tess),
+                "vocabulary": vocab_list_tess,
+                "confidence": 0.7 if tess_vocab else 0,
+                "success": len(vocab_list_tess) > 0,
+                "error": tess_result.get("error"),
+                "word_count": tess_result.get("word_count", 0),
+                "columns_detected": len(tess_result.get("columns", [])),
+            }
+            all_vocab_sets["tesseract"] = {
+                (v["english"].lower().strip(), v["german"].lower().strip())
+                for v in vocab_list_tess if v["english"] and v["german"]
+            }
+
+            # Fuzzy-match: attach Tesseract bounding boxes to Vision LLM results
+            if "vision_llm" in methods_results and methods_results["vision_llm"]["success"]:
+                llm_vocab_with_bbox = match_positions_to_vocab(
+                    tess_words,
+                    methods_results["vision_llm"]["vocabulary"],
+                    tess_result.get("image_width", 1),
+                    tess_result.get("image_height", 1),
+                )
+                methods_results["vision_llm"]["vocabulary"] = llm_vocab_with_bbox
+
+        except Exception as e:
+            logger.error(f"Tesseract failed: {e}")
+            import traceback
+            logger.debug(traceback.format_exc())
+            methods_results["tesseract"] = {
+                "name": "Tesseract OCR",
+                "model": "tesseract-ocr",
+                "duration_seconds": 0,
+                "vocabulary_count": 0,
+                "vocabulary": [],
+                "confidence": 0,
+                "success": False,
+                "error": str(e),
+            }
+            all_vocab_sets["tesseract"] = set()
+
+    # --- Method: CV Pipeline (Document Reconstruction) ---
+    if CV_PIPELINE_AVAILABLE:
+        try:
+            start = time.time()
+            cv_result = await run_cv_pipeline(pdf_data=pdf_data, page_number=page_number)
+            duration = time.time() - start
+
+            cv_vocab = cv_result.vocabulary if not cv_result.error else []
+            vocab_list_cv = []
+            for v in cv_vocab:
+                vocab_list_cv.append({
+                    "english": v.get("english", ""),
+                    "german": v.get("german", ""),
+                    "example": v.get("example", ""),
+                })
+
+            methods_results["cv_pipeline"] = {
+                "name": "CV Pipeline (Document Reconstruction)",
+                "model": "opencv + tesseract (multi-pass)",
+                "duration_seconds": round(duration, 1),
+                "vocabulary_count": len(vocab_list_cv),
+                "vocabulary": vocab_list_cv,
+                "confidence": 0.8 if cv_vocab else 0,
+                "success": len(vocab_list_cv) > 0,
+                "error": cv_result.error,
+                "word_count": cv_result.word_count,
+                "columns_detected": cv_result.columns_detected,
+                "stages": cv_result.stages,
+            }
+            all_vocab_sets["cv_pipeline"] = {
+                (v["english"].lower().strip(), v["german"].lower().strip())
+                for v in vocab_list_cv if v["english"] and v["german"]
+            }
+
+        except Exception as e:
+            logger.error(f"CV Pipeline failed: {e}")
+            import traceback
+            logger.debug(traceback.format_exc())
+            methods_results["cv_pipeline"] = {
+                "name": "CV Pipeline (Document Reconstruction)",
+                "model": "opencv + tesseract (multi-pass)",
+                "duration_seconds": 0,
+                "vocabulary_count": 0,
+                "vocabulary": [],
+                "confidence": 0,
+                "success": False,
+                "error": str(e),
+            }
+            all_vocab_sets["cv_pipeline"] = set()
+
+    # --- Build comparison ---
+    all_unique = set()
+    for vs in all_vocab_sets.values():
+        all_unique |= vs
+
+    found_by_all = []
+    found_by_some = []
+    for english, german in sorted(all_unique):
+        found_in = [m for m, vs in all_vocab_sets.items() if (english, german) in vs]
+        entry = {"english": english, "german": german, "methods": found_in}
+        if len(found_in) == len(all_vocab_sets):
+            found_by_all.append(entry)
+        else:
+            found_by_some.append(entry)
+
+    total_methods = max(len(all_vocab_sets), 1)
+    agreement_rate = len(found_by_all) / max(len(all_unique), 1) if all_unique else 0
+
+    # Find best method
+    best_method = max(all_vocab_sets, key=lambda m: len(all_vocab_sets[m])) if all_vocab_sets else "vision_llm"
+
+    return {
+        "session_id": session_id,
+        "page_number": page_number,
+        "methods": methods_results,
+        "comparison": {
+            "found_by_all_methods": found_by_all,
+            "found_by_some_methods": found_by_some,
+            "total_unique_vocabulary": len(all_unique),
+            "agreement_rate": agreement_rate,
+        },
+        "recommendation": {
+            "best_method": best_method,
+            "reason": f"{len(all_vocab_sets.get(best_method, set()))} Vokabeln erkannt mit hoher Konfidenz",
+        },
+    }
+
+
+@compare_router.post("/sessions/{session_id}/analyze-grid/{page_number}")
+async def analyze_grid(session_id: str, page_number: int, use_tesseract: bool = Query(True)):
+    """
+    Analyze the grid/table structure of a vocabulary page.
+
+    Hybrid approach:
+    1. If Tesseract bounding boxes are available (from compare-ocr), use them for
+       real spatial positions via GridDetectionService.
+    2. Otherwise fall back to Vision LLM for grid structure detection.
+
+    page_number is 0-indexed.
+    Returns GridData structure expected by the frontend GridOverlay component.
+    """
+    import httpx
+
+    logger.info(f"Grid analysis for session {session_id}, page {page_number} (use_tesseract={use_tesseract})")
+
+    if session_id not in _get_sessions():
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    session = _get_sessions()[session_id]
+    pdf_data = session.get("pdf_data")
+
+    if not pdf_data:
+        raise HTTPException(status_code=400, detail="No PDF uploaded for this session")
+
+    page_count = session.get("pdf_page_count", 1)
+    if page_number < 0 or page_number >= page_count:
+        raise HTTPException(status_code=400, detail=f"Invalid page number.")
+
+    # Convert page to image
+    image_data = await convert_pdf_page_to_image(pdf_data, page_number, thumbnail=False)
+
+    # --- Strategy 1: Use Tesseract bounding boxes + GridDetectionService ---
+    tess_page_data = session.get(f"tesseract_page_{page_number}")
+
+    if use_tesseract and TESSERACT_AVAILABLE and GRID_SERVICE_AVAILABLE:
+        try:
+            # Run Tesseract if not already cached
+            if not tess_page_data:
+                logger.info("Running Tesseract for grid analysis (not cached)")
+                from tesseract_vocab_extractor import run_tesseract_pipeline as _run_tess
+                tess_page_data = await _run_tess(image_data, lang="eng+deu")
+                session[f"tesseract_page_{page_number}"] = tess_page_data
+                session["tesseract_words"] = tess_page_data.get("words", [])
+                session["tesseract_image_width"] = tess_page_data.get("image_width", 0)
+                session["tesseract_image_height"] = tess_page_data.get("image_height", 0)
+
+            tess_words = tess_page_data.get("words", [])
+            img_w = tess_page_data.get("image_width", 0)
+            img_h = tess_page_data.get("image_height", 0)
+
+            if tess_words and img_w > 0 and img_h > 0:
+                service = GridDetectionService()
+                regions = service.convert_tesseract_regions(tess_words, img_w, img_h)
+
+                if regions:
+                    grid_result = service.detect_grid(regions)
+                    grid_dict = grid_result.to_dict()
+
+                    # Merge LLM text if available (better quality than Tesseract text)
+                    # The LLM vocab was stored during compare-ocr
+                    grid_dict["source"] = "tesseract+grid_service"
+                    grid_dict["word_count"] = len(tess_words)
+
+                    logger.info(f"Tesseract grid: {grid_result.rows}x{grid_result.columns}, "
+                                f"{grid_result.stats.get('recognized', 0)} recognized")
+
+                    return {"success": True, "grid": grid_dict}
+
+            logger.info("Tesseract data insufficient, falling back to LLM")
+
+        except Exception as e:
+            logger.warning(f"Tesseract grid analysis failed, falling back to LLM: {e}")
+            import traceback
+            logger.debug(traceback.format_exc())
+
+    # --- Strategy 2: Fall back to Vision LLM ---
+    image_base64 = base64.b64encode(image_data).decode("utf-8")
+
+    grid_prompt = """Analyze this textbook page image. It contains a vocabulary table/grid.
+
+Your task: Identify the TABLE STRUCTURE and extract each cell's content.
+
+Return a JSON object with this EXACT structure:
+{
+  "rows": <number of rows>,
+  "columns": <number of columns>,
+  "column_types": ["english", "german", "example"],
+  "entries": [
+    {
+      "row": 0,
+      "col": 0,
+      "text": "the word or phrase in this cell",
+      "column_type": "english",
+      "confidence": 0.95
+    }
+  ]
+}
+
+Rules:
+- row and col are 0-indexed
+- column_type is one of: "english", "german", "example", "unknown"
+- Detect whether each column contains English words, German translations, or example sentences
+- Include ALL non-empty cells
+- confidence is 0.0-1.0 based on how clear the text is
+- If a cell is empty, don't include it
+- Return ONLY the JSON, no other text"""
+
+    try:
+        import asyncio
+
+        raw_text = ""
+        max_retries = 3
+        for attempt in range(max_retries):
+            async with httpx.AsyncClient(timeout=300.0) as client:
+                response = await client.post(
+                    f"{OLLAMA_URL}/api/chat",
+                    json={
+                        "model": VISION_MODEL,
+                        "messages": [{"role": "user", "content": grid_prompt, "images": [image_base64]}],
+                        "stream": False,
+                        "options": {"temperature": 0.1, "num_predict": 8192},
+                    },
+                    timeout=300.0,
+                )
+
+            if response.status_code == 500 and attempt < max_retries - 1:
+                wait_time = 10 * (attempt + 1)
+                logger.warning(f"Ollama returned 500, retrying in {wait_time}s (attempt {attempt + 1}/{max_retries})")
+                await asyncio.sleep(wait_time)
+                continue
+            elif response.status_code != 200:
+                error_detail = response.text[:200] if response.text else "Unknown error"
+                return {"success": False, "error": f"Ollama Fehler ({response.status_code}): {error_detail}. Bitte erneut versuchen - evtl. laeuft noch ein anderer OCR-Request."}
+
+            raw_text = response.json().get("message", {}).get("content", "")
+            break
+
+        # Parse JSON from response
+        import re
+        json_match = re.search(r'\{[\s\S]*\}', raw_text)
+        if not json_match:
+            return {"success": False, "error": "Could not parse grid structure from LLM response"}
+
+        grid_raw = json.loads(json_match.group())
+
+        num_rows = grid_raw.get("rows", 0)
+        num_cols = grid_raw.get("columns", 0)
+        column_types = grid_raw.get("column_types", [])
+        entries = grid_raw.get("entries", [])
+
+        if num_rows == 0 or num_cols == 0:
+            return {"success": False, "error": "No grid structure detected"}
+
+        # Ensure column_types has the right length
+        while len(column_types) < num_cols:
+            column_types.append("unknown")
+
+        # Build cell grid with percentage-based coordinates
+        row_height = 100.0 / num_rows
+        col_width = 100.0 / num_cols
+
+        # Track which cells have content
+        cell_map = {}
+        for entry in entries:
+            r = entry.get("row", 0)
+            c = entry.get("col", 0)
+            cell_map[(r, c)] = entry
+
+        cells = []
+        recognized_count = 0
+        empty_count = 0
+        problematic_count = 0
+
+        for r in range(num_rows):
+            row_cells = []
+            for c in range(num_cols):
+                x = c * col_width
+                y = r * row_height
+
+                if (r, c) in cell_map:
+                    entry = cell_map[(r, c)]
+                    text = entry.get("text", "").strip()
+                    conf = entry.get("confidence", 0.8)
+                    col_type = entry.get("column_type", column_types[c] if c < len(column_types) else "unknown")
+
+                    if text:
+                        status = "recognized" if conf >= 0.5 else "problematic"
+                        if status == "recognized":
+                            recognized_count += 1
+                        else:
+                            problematic_count += 1
+                    else:
+                        status = "empty"
+                        empty_count += 1
+                else:
+                    text = ""
+                    conf = 0.0
+                    col_type = column_types[c] if c < len(column_types) else "unknown"
+                    status = "empty"
+                    empty_count += 1
+
+                row_cells.append({
+                    "row": r,
+                    "col": c,
+                    "x": round(x, 2),
+                    "y": round(y, 2),
+                    "width": round(col_width, 2),
+                    "height": round(row_height, 2),
+                    "text": text,
+                    "confidence": conf,
+                    "status": status,
+                    "column_type": col_type,
+                })
+            cells.append(row_cells)
+
+        total = num_rows * num_cols
+        coverage = (recognized_count + problematic_count) / max(total, 1)
+
+        # Column and row boundaries as percentages
+        col_boundaries = [round(c * col_width, 2) for c in range(num_cols + 1)]
+        row_boundaries = [round(r * row_height, 2) for r in range(num_rows + 1)]
+
+        grid_data = {
+            "rows": num_rows,
+            "columns": num_cols,
+            "cells": cells,
+            "column_types": column_types,
+            "column_boundaries": col_boundaries,
+            "row_boundaries": row_boundaries,
+            "deskew_angle": 0.0,
+            "source": "vision_llm",
+            "stats": {
+                "recognized": recognized_count,
+                "problematic": problematic_count,
+                "empty": empty_count,
+                "manual": 0,
+                "total": total,
+                "coverage": round(coverage, 3),
+            },
+        }
+
+        return {"success": True, "grid": grid_data}
+
+    except httpx.TimeoutException:
+        logger.error("Grid analysis timed out")
+        return {"success": False, "error": "Grid-Analyse Timeout (Ollama zu langsam)"}
+    except Exception as e:
+        logger.error(f"Grid analysis failed: {e}")
+        import traceback
+        logger.debug(traceback.format_exc())
+        return {"success": False, "error": f"Grid-Analyse fehlgeschlagen: {str(e)}"}

klausur-service/backend/vocab/worksheet/extraction.py

@@ -0,0 +1,325 @@
+"""Vocabulary extraction from images using Vision LLM and hybrid OCR+LLM.
+
+Contains:
+- VOCAB_EXTRACTION_PROMPT: Prompt template for Vision LLM extraction
+- extract_vocabulary_from_image(): Core extraction (hybrid or Vision LLM)
+- _get_demo_vocabulary(): Demo data for testing
+- parse_vocabulary_json(): Robust JSON parsing with 4-strategy fallback
+"""
+
+import base64
+import json
+import logging
+import os
+import re
+import uuid
+from typing import List
+
+import httpx
+
+from .models import VocabularyEntry
+
+logger = logging.getLogger(__name__)
+
+# Ollama Configuration
+OLLAMA_URL = os.getenv("OLLAMA_URL", "http://host.docker.internal:11434")
+VISION_MODEL = os.getenv("OLLAMA_VISION_MODEL", "qwen2.5vl:32b")
+
+
+# =============================================================================
+# Vision LLM Vocabulary Extraction
+# =============================================================================
+
+VOCAB_EXTRACTION_PROMPT = """Analysiere dieses Bild einer Vokabelliste aus einem Schulbuch.
+
+AUFGABE: Extrahiere alle Vokabeleintraege in folgendem JSON-Format:
+
+{
+  "vocabulary": [
+    {
+      "english": "to improve",
+      "german": "verbessern",
+      "example": "I want to improve my English."
+    }
+  ]
+}
+
+REGELN:
+1. Erkenne das typische 3-Spalten-Layout: Englisch | Deutsch | Beispielsatz
+2. Behalte die exakte Schreibweise bei
+3. Bei fehlenden Beispielsaetzen: "example": null
+4. Ignoriere Seitenzahlen, Ueberschriften, Kapitelnummern
+5. Gib NUR valides JSON zurueck, keine Erklaerungen
+6. Wenn Wortarten angegeben sind (n, v, adj), extrahiere sie als "word_type"
+
+Beispiel-Output:
+{
+  "vocabulary": [
+    {"english": "achievement", "german": "Leistung, Errungenschaft", "example": "Her achievements were impressive.", "word_type": "n"},
+    {"english": "to achieve", "german": "erreichen, erzielen", "example": "She achieved her goals.", "word_type": "v"}
+  ]
+}"""
+
+
+async def extract_vocabulary_from_image(
+    image_data: bytes,
+    filename: str,
+    page_number: int = 0,
+    use_hybrid: bool = False  # DISABLED: PaddleOCR crashes on ARM64 Mac Mini
+) -> tuple[List[VocabularyEntry], float, str]:
+    """
+    Extract vocabulary from an image using hybrid OCR+LLM or Vision LLM (default).
+
+    Args:
+        image_data: Image bytes
+        filename: Original filename for logging
+        page_number: 0-indexed page number for error messages
+        use_hybrid: If True, use PaddleOCR + LLM (faster, more accurate for printed text)
+                   If False, use Vision LLM (slower, better for complex layouts)
+
+    Returns:
+        Tuple of (vocabulary_entries, confidence, error_message)
+        error_message is empty string on success
+    """
+
+    # ==========================================================================
+    # HYBRID APPROACH (Default): PaddleOCR + LLM Gateway
+    # ==========================================================================
+    if use_hybrid:
+        try:
+            from hybrid_vocab_extractor import extract_vocabulary_hybrid
+            logger.info(f"Using HYBRID extraction for {filename} (PaddleOCR + LLM)")
+
+            vocab_dicts, confidence, error = await extract_vocabulary_hybrid(image_data, page_number)
+
+            if error:
+                logger.warning(f"Hybrid extraction had issues: {error}")
+                # Fall through to Vision LLM fallback
+            elif vocab_dicts:
+                # Convert dicts to VocabularyEntry objects
+                vocabulary = [
+                    VocabularyEntry(
+                        id=str(uuid.uuid4()),
+                        english=v.get("english", ""),
+                        german=v.get("german", ""),
+                        example_sentence=v.get("example"),
+                        source_page=page_number + 1
+                    )
+                    for v in vocab_dicts
+                    if v.get("english") and v.get("german")
+                ]
+                logger.info(f"Hybrid extraction: {len(vocabulary)} entries from {filename}")
+                return vocabulary, confidence, ""
+
+        except ImportError as e:
+            logger.warning(f"Hybrid extractor not available: {e}. Falling back to Vision LLM.")
+        except Exception as e:
+            logger.warning(f"Hybrid extraction failed: {e}. Falling back to Vision LLM.")
+            import traceback
+            logger.debug(traceback.format_exc())
+
+    # ==========================================================================
+    # FALLBACK: Vision LLM (Ollama llama3.2-vision)
+    # ==========================================================================
+    logger.info(f"Using VISION LLM extraction for {filename}")
+
+    try:
+        # First check if Ollama is available
+        async with httpx.AsyncClient(timeout=10.0) as check_client:
+            try:
+                health_response = await check_client.get(f"{OLLAMA_URL}/api/tags")
+                if health_response.status_code != 200:
+                    logger.error(f"Ollama not available at {OLLAMA_URL}")
+                    return [], 0.0, f"Seite {page_number + 1}: Ollama nicht verfuegbar"
+            except Exception as e:
+                logger.error(f"Ollama health check failed: {e}")
+                return [], 0.0, f"Seite {page_number + 1}: Verbindung zu Ollama fehlgeschlagen"
+
+        image_base64 = base64.b64encode(image_data).decode("utf-8")
+
+        payload = {
+            "model": VISION_MODEL,
+            "messages": [
+                {
+                    "role": "user",
+                    "content": VOCAB_EXTRACTION_PROMPT,
+                    "images": [image_base64]
+                }
+            ],
+            "stream": False,
+            "options": {
+                "temperature": 0.1,
+                "num_predict": 4096,
+            }
+        }
+
+        logger.info(f"Extracting vocabulary from {filename} ({len(image_data)} bytes) using {VISION_MODEL}")
+
+        # Increased timeout for Vision models (they can be slow)
+        async with httpx.AsyncClient(timeout=600.0) as client:
+            response = await client.post(
+                f"{OLLAMA_URL}/api/chat",
+                json=payload,
+                timeout=300.0  # 5 minutes per page
+            )
+            response.raise_for_status()
+
+            data = response.json()
+            extracted_text = data.get("message", {}).get("content", "")
+
+        logger.info(f"Ollama response received: {len(extracted_text)} chars")
+
+        # Parse JSON from response
+        vocabulary = parse_vocabulary_json(extracted_text)
+
+        # Set source_page for each entry
+        for v in vocabulary:
+            v.source_page = page_number + 1
+
+        # Estimate confidence
+        confidence = 0.85 if len(vocabulary) > 0 else 0.1
+
+        logger.info(f"Vision LLM extracted {len(vocabulary)} vocabulary entries from {filename}")
+
+        return vocabulary, confidence, ""
+
+    except httpx.TimeoutException:
+        logger.error(f"Ollama request timed out for {filename} (model: {VISION_MODEL})")
+        return [], 0.0, f"Seite {page_number + 1}: Timeout - Verarbeitung dauerte zu lange"
+    except Exception as e:
+        logger.error(f"Vocabulary extraction failed for {filename}: {e}")
+        import traceback
+        logger.error(traceback.format_exc())
+        return [], 0.0, f"Seite {page_number + 1}: Fehler - {str(e)[:50]}"
+
+
+def _get_demo_vocabulary() -> List[VocabularyEntry]:
+    """Return demo vocabulary for testing when Vision LLM is not available."""
+    demo_entries = [
+        {"english": "to achieve", "german": "erreichen, erzielen", "example": "She achieved her goals."},
+        {"english": "achievement", "german": "Leistung, Errungenschaft", "example": "That was a great achievement."},
+        {"english": "improve", "german": "verbessern", "example": "I want to improve my English."},
+        {"english": "improvement", "german": "Verbesserung", "example": "There has been a lot of improvement."},
+        {"english": "success", "german": "Erfolg", "example": "The project was a success."},
+        {"english": "successful", "german": "erfolgreich", "example": "She is a successful businesswoman."},
+        {"english": "fail", "german": "scheitern, durchfallen", "example": "Don't be afraid to fail."},
+        {"english": "failure", "german": "Misserfolg, Versagen", "example": "Failure is part of learning."},
+    ]
+    return [
+        VocabularyEntry(
+            id=str(uuid.uuid4()),
+            english=e["english"],
+            german=e["german"],
+            example_sentence=e.get("example"),
+        )
+        for e in demo_entries
+    ]
+
+
+def parse_vocabulary_json(text: str) -> List[VocabularyEntry]:
+    """Parse vocabulary JSON from LLM response with robust error handling."""
+
+    def clean_json_string(s: str) -> str:
+        """Clean a JSON string by removing control characters and fixing common issues."""
+        # Remove control characters except newlines and tabs
+        s = re.sub(r'[\x00-\x08\x0b\x0c\x0e-\x1f]', '', s)
+        # Replace unescaped newlines within strings with space
+        # This is a simplistic approach - replace actual newlines with escaped ones
+        s = s.replace('\n', '\\n').replace('\r', '\\r').replace('\t', '\\t')
+        return s
+
+    def try_parse_json(json_str: str) -> dict:
+        """Try multiple strategies to parse JSON."""
+        # Strategy 1: Direct parse
+        try:
+            return json.loads(json_str)
+        except json.JSONDecodeError:
+            pass
+
+        # Strategy 2: Clean and parse
+        try:
+            cleaned = clean_json_string(json_str)
+            return json.loads(cleaned)
+        except json.JSONDecodeError:
+            pass
+
+        # Strategy 3: Try to fix common issues
+        try:
+            # Remove trailing commas before } or ]
+            fixed = re.sub(r',(\s*[}\]])', r'\1', json_str)
+            # Fix unquoted keys
+            fixed = re.sub(r'(\{|\,)\s*([a-zA-Z_][a-zA-Z0-9_]*)\s*:', r'\1"\2":', fixed)
+            return json.loads(fixed)
+        except json.JSONDecodeError:
+            pass
+
+        return None
+
+    try:
+        # Find JSON in response (may have extra text)
+        start = text.find('{')
+        end = text.rfind('}') + 1
+
+        if start == -1 or end == 0:
+            logger.warning("No JSON found in response")
+            return []
+
+        json_str = text[start:end]
+        data = try_parse_json(json_str)
+
+        if data is None:
+            # Strategy 4: Extract vocabulary entries using regex as fallback
+            logger.warning("JSON parsing failed, trying regex extraction")
+            vocabulary = []
+            # Match patterns like {"english": "...", "german": "...", ...}
+            pattern = r'\{\s*"english"\s*:\s*"([^"]*?)"\s*,\s*"german"\s*:\s*"([^"]*?)"(?:\s*,\s*"example"\s*:\s*(?:"([^"]*?)"|null))?'
+            matches = re.findall(pattern, text, re.IGNORECASE | re.DOTALL)
+
+            for match in matches:
+                english = match[0].strip() if match[0] else ""
+                german = match[1].strip() if match[1] else ""
+                example = match[2].strip() if len(match) > 2 and match[2] else None
+
+                if english and german:
+                    vocab_entry = VocabularyEntry(
+                        id=str(uuid.uuid4()),
+                        english=english,
+                        german=german,
+                        example_sentence=example,
+                    )
+                    vocabulary.append(vocab_entry)
+
+            if vocabulary:
+                logger.info(f"Regex extraction found {len(vocabulary)} entries")
+            return vocabulary
+
+        # Normal JSON parsing succeeded
+        vocabulary = []
+        for i, entry in enumerate(data.get("vocabulary", [])):
+            english = entry.get("english", "").strip()
+            german = entry.get("german", "").strip()
+
+            # Skip entries that look like hallucinations (very long or containing unusual patterns)
+            if len(english) > 100 or len(german) > 200:
+                logger.warning(f"Skipping suspicious entry: {english[:50]}...")
+                continue
+
+            if not english or not german:
+                continue
+
+            vocab_entry = VocabularyEntry(
+                id=str(uuid.uuid4()),
+                english=english,
+                german=german,
+                example_sentence=entry.get("example"),
+                word_type=entry.get("word_type"),
+            )
+            vocabulary.append(vocab_entry)
+
+        return vocabulary
+
+    except Exception as e:
+        logger.error(f"Failed to parse vocabulary JSON: {e}")
+        import traceback
+        logger.error(traceback.format_exc())
+        return []

klausur-service/backend/vocab/worksheet/generation.py

@@ -0,0 +1,258 @@
+"""
+Vocabulary Worksheet Generation — HTML/PDF generation and PDF utilities.
+
+Extracted from vocab_worksheet_api.py to keep modules under 500 LOC.
+
+Functions:
+  - generate_worksheet_html(): Build HTML for various worksheet types
+  - generate_worksheet_pdf():  Convert HTML to PDF via WeasyPrint
+  - get_pdf_page_count():      Count pages in a PDF (PyMuPDF)
+  - convert_pdf_page_to_image(): Render single PDF page to PNG
+  - convert_pdf_to_images():     Render multiple PDF pages to PNG
+"""
+
+import logging
+from typing import List
+
+from fastapi import HTTPException
+
+from .models import VocabularyEntry, WorksheetType
+
+logger = logging.getLogger(__name__)
+
+# Optional dependency: WeasyPrint
+try:
+    from weasyprint import HTML as _WeasyHTML
+    WEASYPRINT_AVAILABLE = True
+except (ImportError, OSError):
+    WEASYPRINT_AVAILABLE = False
+    logger.warning("WeasyPrint not available")
+
+# Optional dependency: PyMuPDF
+try:
+    import fitz  # PyMuPDF
+    FITZ_AVAILABLE = True
+except ImportError:
+    FITZ_AVAILABLE = False
+    logger.warning("PyMuPDF (fitz) not available")
+
+
+# =============================================================================
+# Worksheet HTML Generation
+# =============================================================================
+
+def generate_worksheet_html(
+    vocabulary: List[VocabularyEntry],
+    worksheet_type: WorksheetType,
+    title: str,
+    show_solutions: bool = False,
+    repetitions: int = 3,
+    line_height: str = "normal"
+) -> str:
+    """Generate HTML for a worksheet."""
+
+    # Line height CSS
+    line_heights = {
+        "normal": "2.5em",
+        "large": "3.5em",
+        "extra-large": "4.5em"
+    }
+    lh = line_heights.get(line_height, "2.5em")
+
+    html = f"""<!DOCTYPE html>
+<html>
+<head>
+    <meta charset="UTF-8">
+    <style>
+        @page {{ size: A4; margin: 2cm; }}
+        body {{ font-family: 'Segoe UI', Arial, sans-serif; font-size: 14px; }}
+        h1 {{ font-size: 24px; margin-bottom: 10px; }}
+        .meta {{ color: #666; margin-bottom: 20px; }}
+        .name-line {{ margin-bottom: 30px; }}
+        .vocab-table {{ width: 100%; border-collapse: collapse; }}
+        .vocab-table td {{ padding: 8px; border-bottom: 1px solid #ddd; line-height: {lh}; }}
+        .vocab-word {{ width: 40%; font-weight: 500; }}
+        .vocab-blank {{ width: 60%; border-bottom: 2px dotted #999; }}
+        .vocab-answer {{ width: 60%; color: #2563eb; }}
+        .gap {{ border-bottom: 2px solid #333; min-width: 100px; display: inline-block; }}
+        .hint {{ color: #666; font-style: italic; font-size: 12px; }}
+        .section {{ margin-top: 30px; }}
+        .section-title {{ font-size: 16px; font-weight: 600; margin-bottom: 15px; color: #374151; }}
+    </style>
+</head>
+<body>
+    <h1>{title}</h1>
+    <div class="name-line">Name: _________________________ Datum: _____________</div>
+"""
+
+    if worksheet_type == WorksheetType.EN_TO_DE:
+        html += '<div class="section"><div class="section-title">Uebersetze ins Deutsche:</div>'
+        html += '<table class="vocab-table">'
+        for entry in vocabulary:
+            if show_solutions:
+                html += f'<tr><td class="vocab-word">{entry.english}</td><td class="vocab-answer">{entry.german}</td></tr>'
+            else:
+                html += f'<tr><td class="vocab-word">{entry.english}</td><td class="vocab-blank"></td></tr>'
+        html += '</table></div>'
+
+    elif worksheet_type == WorksheetType.DE_TO_EN:
+        html += '<div class="section"><div class="section-title">Uebersetze ins Englische:</div>'
+        html += '<table class="vocab-table">'
+        for entry in vocabulary:
+            if show_solutions:
+                html += f'<tr><td class="vocab-word">{entry.german}</td><td class="vocab-answer">{entry.english}</td></tr>'
+            else:
+                html += f'<tr><td class="vocab-word">{entry.german}</td><td class="vocab-blank"></td></tr>'
+        html += '</table></div>'
+
+    elif worksheet_type == WorksheetType.COPY_PRACTICE:
+        html += '<div class="section"><div class="section-title">Schreibe jedes Wort mehrmals:</div>'
+        html += '<table class="vocab-table">'
+        for entry in vocabulary:
+            html += f'<tr><td class="vocab-word">{entry.english}</td>'
+            html += '<td class="vocab-blank">'
+            if show_solutions:
+                html += f' {entry.english} ' * repetitions
+            html += '</td></tr>'
+        html += '</table></div>'
+
+    elif worksheet_type == WorksheetType.GAP_FILL:
+        entries_with_examples = [e for e in vocabulary if e.example_sentence]
+        if entries_with_examples:
+            html += '<div class="section"><div class="section-title">Fuege das passende Wort ein:</div>'
+            for i, entry in enumerate(entries_with_examples, 1):
+                # Create gap sentence by removing the English word
+                gap_sentence = entry.example_sentence
+                for word in entry.english.split():
+                    if word.lower() in gap_sentence.lower():
+                        gap_sentence = gap_sentence.replace(word, '<span class="gap"></span>')
+                        gap_sentence = gap_sentence.replace(word.capitalize(), '<span class="gap"></span>')
+                        gap_sentence = gap_sentence.replace(word.lower(), '<span class="gap"></span>')
+                        break
+
+                html += f'<p>{i}. {gap_sentence}</p>'
+                if show_solutions:
+                    html += f'<p class="hint">Loesung: {entry.english}</p>'
+                else:
+                    html += f'<p class="hint">({entry.german})</p>'
+            html += '</div>'
+
+    html += '</body></html>'
+    return html
+
+
+# =============================================================================
+# Worksheet PDF Generation
+# =============================================================================
+
+async def generate_worksheet_pdf(html: str) -> bytes:
+    """Generate PDF from HTML using WeasyPrint."""
+    try:
+        from weasyprint import HTML
+        pdf_bytes = HTML(string=html).write_pdf()
+        return pdf_bytes
+    except ImportError:
+        logger.warning("WeasyPrint not available, returning HTML")
+        return html.encode('utf-8')
+    except Exception as e:
+        logger.error(f"PDF generation failed: {e}")
+        raise
+
+
+# =============================================================================
+# PDF Utilities (PyMuPDF)
+# =============================================================================
+
+def get_pdf_page_count(pdf_data: bytes) -> int:
+    """Get the number of pages in a PDF."""
+    try:
+        import fitz
+        pdf_document = fitz.open(stream=pdf_data, filetype="pdf")
+        count = pdf_document.page_count
+        pdf_document.close()
+        return count
+    except Exception as e:
+        logger.error(f"Failed to get PDF page count: {e}")
+        return 0
+
+
+async def convert_pdf_page_to_image(pdf_data: bytes, page_number: int = 0, thumbnail: bool = False) -> bytes:
+    """Convert a specific page of PDF to PNG image using PyMuPDF.
+
+    Args:
+        pdf_data: PDF file as bytes
+        page_number: 0-indexed page number
+        thumbnail: If True, return a smaller thumbnail image
+    """
+    try:
+        import fitz  # PyMuPDF
+
+        pdf_document = fitz.open(stream=pdf_data, filetype="pdf")
+
+        if pdf_document.page_count == 0:
+            raise ValueError("PDF has no pages")
+
+        if page_number >= pdf_document.page_count:
+            raise ValueError(f"Page {page_number} does not exist (PDF has {pdf_document.page_count} pages)")
+
+        page = pdf_document[page_number]
+
+        # Render page to image
+        # For thumbnails: lower resolution, for OCR: higher resolution
+        zoom = 0.5 if thumbnail else 2.0
+        mat = fitz.Matrix(zoom, zoom)
+        pix = page.get_pixmap(matrix=mat)
+
+        png_data = pix.tobytes("png")
+        pdf_document.close()
+
+        logger.info(f"Converted PDF page {page_number} to PNG: {len(png_data)} bytes (thumbnail={thumbnail})")
+        return png_data
+
+    except ImportError:
+        logger.error("PyMuPDF (fitz) not installed")
+        raise HTTPException(status_code=500, detail="PDF conversion not available - PyMuPDF not installed")
+    except Exception as e:
+        logger.error(f"PDF conversion failed: {e}")
+        raise HTTPException(status_code=400, detail=f"PDF conversion failed: {str(e)}")
+
+
+async def convert_pdf_to_images(pdf_data: bytes, pages: List[int] = None) -> List[bytes]:
+    """Convert multiple pages of PDF to PNG images.
+
+    Args:
+        pdf_data: PDF file as bytes
+        pages: List of 0-indexed page numbers to convert. If None, convert all pages.
+    """
+    try:
+        import fitz
+
+        pdf_document = fitz.open(stream=pdf_data, filetype="pdf")
+
+        if pdf_document.page_count == 0:
+            raise ValueError("PDF has no pages")
+
+        # If no pages specified, convert all
+        if pages is None:
+            pages = list(range(pdf_document.page_count))
+
+        images = []
+        zoom = 2.0
+        mat = fitz.Matrix(zoom, zoom)
+
+        for page_num in pages:
+            if page_num < pdf_document.page_count:
+                page = pdf_document[page_num]
+                pix = page.get_pixmap(matrix=mat)
+                images.append(pix.tobytes("png"))
+
+        pdf_document.close()
+        logger.info(f"Converted {len(images)} PDF pages to images")
+        return images
+
+    except ImportError:
+        logger.error("PyMuPDF (fitz) not installed")
+        raise HTTPException(status_code=500, detail="PDF conversion not available")
+    except Exception as e:
+        logger.error(f"PDF conversion failed: {e}")
+        raise HTTPException(status_code=400, detail=f"PDF conversion failed: {str(e)}")

klausur-service/backend/vocab/worksheet/models.py

@@ -0,0 +1,86 @@
+"""Pydantic models and enums for the Vocab Worksheet API."""
+
+from datetime import datetime
+from enum import Enum
+from typing import List, Optional
+
+from pydantic import BaseModel
+
+
+# =============================================================================
+# Enums
+# =============================================================================
+
+class WorksheetType(str, Enum):
+    EN_TO_DE = "en_to_de"          # English -> German translation
+    DE_TO_EN = "de_to_en"          # German -> English translation
+    COPY_PRACTICE = "copy"         # Write word multiple times
+    GAP_FILL = "gap_fill"          # Fill in the blanks
+    COMBINED = "combined"          # All types combined
+
+
+class SessionStatus(str, Enum):
+    PENDING = "pending"            # Session created, no upload yet
+    PROCESSING = "processing"      # OCR in progress
+    EXTRACTED = "extracted"        # Vocabulary extracted, ready to edit
+    COMPLETED = "completed"        # Worksheet generated
+
+
+# =============================================================================
+# Pydantic Models
+# =============================================================================
+
+class VocabularyEntry(BaseModel):
+    id: str
+    english: str
+    german: str
+    example_sentence: Optional[str] = None
+    example_sentence_gap: Optional[str] = None  # With ___ for gap-fill
+    word_type: Optional[str] = None  # noun, verb, adjective, etc.
+    source_page: Optional[int] = None  # Page number where entry was found (1-indexed)
+
+
+class SessionCreate(BaseModel):
+    name: str
+    description: Optional[str] = None
+    source_language: str = "en"  # Source language (default English)
+    target_language: str = "de"  # Target language (default German)
+
+
+class SessionResponse(BaseModel):
+    id: str
+    name: str
+    description: Optional[str]
+    source_language: str
+    target_language: str
+    status: str
+    vocabulary_count: int
+    image_path: Optional[str]
+    created_at: datetime
+
+
+class VocabularyResponse(BaseModel):
+    session_id: str
+    vocabulary: List[VocabularyEntry]
+    extraction_confidence: Optional[float]
+
+
+class VocabularyUpdate(BaseModel):
+    vocabulary: List[VocabularyEntry]
+
+
+class WorksheetGenerateRequest(BaseModel):
+    worksheet_types: List[WorksheetType]
+    title: Optional[str] = None
+    include_solutions: bool = True
+    repetitions: int = 3  # For copy practice
+    line_height: str = "normal"  # normal, large, extra-large
+
+
+class WorksheetResponse(BaseModel):
+    id: str
+    session_id: str
+    worksheet_types: List[str]
+    pdf_path: str
+    solution_path: Optional[str]
+    generated_at: datetime

klausur-service/backend/vocab/worksheet/ocr.py

@@ -0,0 +1,481 @@
+"""
+Vocab Worksheet OCR Pipeline — full Kombi OCR pipeline for a single page.
+
+Extracted from vocab_worksheet_api.py to keep file sizes manageable.
+
+Pipeline steps:
+  orientation → deskew → dewarp → crop → scan-quality → enhance →
+  dual-engine OCR (RapidOCR + Tesseract) → merge → grid-build →
+  vocab extraction → row merging
+"""
+
+import logging
+import uuid
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Optional heavy dependencies (not available in every environment)
+# ---------------------------------------------------------------------------
+
+try:
+    import cv2
+    import numpy as np
+except ImportError:
+    cv2 = None  # type: ignore[assignment]
+    np = None  # type: ignore[assignment]
+    logger.warning("cv2 / numpy not available — OCR pipeline disabled")
+
+try:
+    from PIL import Image
+except ImportError:
+    Image = None  # type: ignore[assignment]
+
+try:
+    import pytesseract
+except ImportError:
+    pytesseract = None  # type: ignore[assignment]
+
+# CV pipeline helpers
+try:
+    from cv_vocab_pipeline import (
+        deskew_two_pass,
+        dewarp_image,
+        detect_and_fix_orientation,
+        _cells_to_vocab_entries,
+        _fix_phonetic_brackets,
+    )
+except ImportError:
+    deskew_two_pass = None  # type: ignore[assignment]
+    dewarp_image = None  # type: ignore[assignment]
+    detect_and_fix_orientation = None  # type: ignore[assignment]
+    _cells_to_vocab_entries = None  # type: ignore[assignment]
+    _fix_phonetic_brackets = None  # type: ignore[assignment]
+
+try:
+    from cv_cell_grid import (
+        _merge_wrapped_rows,
+        _merge_phonetic_continuation_rows,
+        _merge_continuation_rows,
+    )
+except ImportError:
+    _merge_wrapped_rows = None  # type: ignore[assignment]
+    _merge_phonetic_continuation_rows = None  # type: ignore[assignment]
+    _merge_continuation_rows = None  # type: ignore[assignment]
+
+try:
+    from cv_ocr_engines import ocr_region_rapid
+except ImportError:
+    ocr_region_rapid = None  # type: ignore[assignment]
+
+try:
+    from cv_vocab_types import PageRegion
+except ImportError:
+    PageRegion = None  # type: ignore[assignment]
+
+try:
+    from ocr_pipeline_ocr_merge import (
+        _split_paddle_multi_words,
+        _merge_paddle_tesseract,
+        _deduplicate_words,
+    )
+except ImportError:
+    _split_paddle_multi_words = None  # type: ignore[assignment]
+    _merge_paddle_tesseract = None  # type: ignore[assignment]
+    _deduplicate_words = None  # type: ignore[assignment]
+
+try:
+    from cv_words_first import build_grid_from_words
+except ImportError:
+    build_grid_from_words = None  # type: ignore[assignment]
+
+try:
+    from ocr_pipeline_session_store import (
+        create_session_db as create_pipeline_session_db,
+        update_session_db as update_pipeline_session_db,
+    )
+except ImportError:
+    create_pipeline_session_db = None  # type: ignore[assignment]
+    update_pipeline_session_db = None  # type: ignore[assignment]
+
+
+# ---------------------------------------------------------------------------
+# Main pipeline function
+# ---------------------------------------------------------------------------
+
+async def _run_ocr_pipeline_for_page(
+    img_bgr: "np.ndarray",
+    page_number: int,
+    vocab_session_id: str,
+    *,
+    ipa_mode: str = "none",
+    syllable_mode: str = "none",
+    enable_enhance: bool = True,
+    max_columns: Optional[int] = 3,
+    override_min_conf: Optional[int] = None,
+) -> tuple:
+    """Run the full Kombi OCR pipeline on a single page and return vocab entries.
+
+    Uses the same pipeline as the admin OCR Kombi pipeline:
+    orientation → deskew → dewarp → crop → dual-engine OCR → grid-build
+    (with pipe-autocorrect, word-gap merge, dictionary detection, etc.)
+
+    Args:
+        img_bgr: BGR numpy array.
+        page_number: 0-indexed page number.
+        vocab_session_id: Vocab session ID for logging.
+        ipa_mode: "none" (default for worksheets), "auto", "all", "en", "de".
+        syllable_mode: "none" (default for worksheets), "auto", "all", "en", "de".
+
+    Returns (entries, rotation_deg) where entries is a list of dicts and
+    rotation_deg is the orientation correction applied (0, 90, 180, 270).
+    """
+    import time as _time
+
+    t_total = _time.time()
+    img_h, img_w = img_bgr.shape[:2]
+    logger.info(f"Kombi Pipeline page {page_number + 1}: image {img_w}x{img_h}")
+
+    # 1. Orientation detection (fix upside-down scans)
+    t0 = _time.time()
+    img_bgr, rotation = detect_and_fix_orientation(img_bgr)
+    if rotation:
+        img_h, img_w = img_bgr.shape[:2]
+        logger.info(f"  orientation: rotated {rotation}° ({_time.time() - t0:.1f}s)")
+    else:
+        logger.info(f"  orientation: OK ({_time.time() - t0:.1f}s)")
+
+    # 2. Create pipeline session in DB (visible in admin Kombi UI)
+    pipeline_session_id = str(uuid.uuid4())
+    try:
+        _, png_buf = cv2.imencode(".png", img_bgr)
+        original_png = png_buf.tobytes()
+        await create_pipeline_session_db(
+            pipeline_session_id,
+            name=f"vocab-ws-{vocab_session_id[:8]}-p{page_number + 1}",
+            filename=f"page_{page_number + 1}.png",
+            original_png=original_png,
+        )
+    except Exception as e:
+        logger.warning(f"Could not create pipeline session in DB: {e}")
+
+    # 3. Three-pass deskew
+    t0 = _time.time()
+    deskewed_bgr, angle_applied, deskew_debug = deskew_two_pass(img_bgr.copy())
+    logger.info(f"  deskew: angle={angle_applied:.2f} ({_time.time() - t0:.1f}s)")
+
+    # 4. Dewarp
+    t0 = _time.time()
+    dewarped_bgr, dewarp_info = dewarp_image(deskewed_bgr)
+    logger.info(f"  dewarp: shear={dewarp_info['shear_degrees']:.3f} ({_time.time() - t0:.1f}s)")
+
+    # 5. Content crop (removes scanner borders, gutter shadows)
+    t0 = _time.time()
+    try:
+        from page_crop import detect_and_crop_page
+        cropped_bgr, crop_result = detect_and_crop_page(dewarped_bgr)
+        if crop_result.get("crop_applied"):
+            dewarped_bgr = cropped_bgr
+            logger.info(f"  crop: applied ({_time.time() - t0:.1f}s)")
+        else:
+            logger.info(f"  crop: skipped ({_time.time() - t0:.1f}s)")
+    except Exception as e:
+        logger.warning(f"  crop: failed ({e}), continuing with uncropped image")
+
+    # 5b. Scan quality assessment
+    scan_quality_report = None
+    try:
+        from scan_quality import score_scan_quality
+        scan_quality_report = score_scan_quality(dewarped_bgr)
+    except Exception as e:
+        logger.warning(f"  scan quality: failed ({e})")
+
+    if override_min_conf:
+        min_ocr_conf = override_min_conf
+    else:
+        min_ocr_conf = scan_quality_report.recommended_min_conf if scan_quality_report else 40
+
+    # 5c. Image enhancement for degraded scans
+    is_degraded = scan_quality_report.is_degraded if scan_quality_report else False
+    if is_degraded and enable_enhance:
+        try:
+            from ocr_image_enhance import enhance_for_ocr
+            dewarped_bgr = enhance_for_ocr(dewarped_bgr, is_degraded=True)
+            logger.info("  enhancement: applied (degraded scan)")
+        except Exception as e:
+            logger.warning(f"  enhancement: failed ({e})")
+
+    # 6. Dual-engine OCR (RapidOCR + Tesseract → merge)
+    t0 = _time.time()
+    img_h, img_w = dewarped_bgr.shape[:2]
+
+    # RapidOCR (local ONNX)
+    try:
+        from cv_ocr_engines import ocr_region_rapid
+        from cv_vocab_types import PageRegion
+        full_region = PageRegion(type="full_page", x=0, y=0, width=img_w, height=img_h)
+        rapid_words = ocr_region_rapid(dewarped_bgr, full_region) or []
+    except Exception as e:
+        logger.warning(f"  RapidOCR failed: {e}")
+        rapid_words = []
+
+    # Tesseract
+    from PIL import Image
+    import pytesseract
+    pil_img = Image.fromarray(cv2.cvtColor(dewarped_bgr, cv2.COLOR_BGR2RGB))
+    data = pytesseract.image_to_data(
+        pil_img, lang="eng+deu", config="--psm 6 --oem 3",
+        output_type=pytesseract.Output.DICT,
+    )
+    tess_words = []
+    for i in range(len(data["text"])):
+        text = str(data["text"][i]).strip()
+        conf_raw = str(data["conf"][i])
+        conf = int(conf_raw) if conf_raw.lstrip("-").isdigit() else -1
+        if not text or conf < min_ocr_conf:
+            continue
+        tess_words.append({
+            "text": text,
+            "left": data["left"][i], "top": data["top"][i],
+            "width": data["width"][i], "height": data["height"][i],
+            "conf": conf,
+        })
+
+    # Merge dual-engine results
+    from ocr_pipeline_ocr_merge import _split_paddle_multi_words, _merge_paddle_tesseract, _deduplicate_words
+    from cv_words_first import build_grid_from_words
+
+    rapid_split = _split_paddle_multi_words(rapid_words) if rapid_words else []
+    if rapid_split or tess_words:
+        merged_words = _merge_paddle_tesseract(rapid_split, tess_words)
+        merged_words = _deduplicate_words(merged_words)
+    else:
+        merged_words = tess_words  # fallback to Tesseract only
+
+    # Build initial grid from merged words
+    cells, columns_meta = build_grid_from_words(merged_words, img_w, img_h, max_columns=max_columns)
+    for cell in cells:
+        cell["ocr_engine"] = "rapid_kombi"
+
+    n_rows = len(set(c["row_index"] for c in cells)) if cells else 0
+    n_cols = len(columns_meta)
+    logger.info(f"  ocr: rapid={len(rapid_words)}, tess={len(tess_words)}, "
+                f"merged={len(merged_words)}, cells={len(cells)} ({_time.time() - t0:.1f}s)")
+
+    # 7. Save word_result to pipeline session (needed by _build_grid_core)
+    word_result = {
+        "cells": cells,
+        "grid_shape": {"rows": n_rows, "cols": n_cols, "total_cells": len(cells)},
+        "columns_used": columns_meta,
+        "layout": "vocab" if {c.get("type") for c in columns_meta} & {"column_en", "column_de"} else "generic",
+        "image_width": img_w,
+        "image_height": img_h,
+        "duration_seconds": 0,
+        "ocr_engine": "rapid_kombi",
+        "raw_tesseract_words": tess_words,
+        "summary": {
+            "total_cells": len(cells),
+            "non_empty_cells": sum(1 for c in cells if c.get("text")),
+        },
+    }
+
+    # Save images + word_result to pipeline session for admin visibility
+    try:
+        _, dsk_buf = cv2.imencode(".png", deskewed_bgr)
+        _, dwp_buf = cv2.imencode(".png", dewarped_bgr)
+        await update_pipeline_session_db(
+            pipeline_session_id,
+            deskewed_png=dsk_buf.tobytes(),
+            dewarped_png=dwp_buf.tobytes(),
+            cropped_png=cv2.imencode(".png", dewarped_bgr)[1].tobytes(),
+            word_result=word_result,
+            deskew_result={"angle_applied": round(angle_applied, 3)},
+            dewarp_result={"shear_degrees": dewarp_info.get("shear_degrees", 0)},
+            current_step=8,
+        )
+    except Exception as e:
+        logger.warning(f"Could not update pipeline session: {e}")
+
+    # 8. Run full grid-build (with pipe-autocorrect, word-gap merge, etc.)
+    t0 = _time.time()
+    try:
+        from grid_editor_api import _build_grid_core
+        session_data = {
+            "word_result": word_result,
+        }
+        grid_result = await _build_grid_core(
+            pipeline_session_id, session_data,
+            ipa_mode=ipa_mode, syllable_mode=syllable_mode,
+        )
+        logger.info(f"  grid-build: {grid_result.get('summary', {}).get('total_cells', 0)} cells "
+                    f"({_time.time() - t0:.1f}s)")
+
+        # Save grid result to pipeline session
+        try:
+            await update_pipeline_session_db(
+                pipeline_session_id,
+                grid_editor_result=grid_result,
+                current_step=11,
+            )
+        except Exception:
+            pass
+
+    except Exception as e:
+        logger.warning(f"  grid-build failed: {e}, falling back to basic grid")
+        grid_result = None
+
+    # 9. Extract vocab entries
+    # Prefer grid-build result (better column detection, more cells) over
+    # the initial build_grid_from_words() which often under-clusters.
+    page_vocabulary = []
+    extraction_source = "none"
+
+    # A) Try grid-build zones first (best quality: 4-column detection, autocorrect)
+    if grid_result and grid_result.get("zones"):
+        for zone in grid_result["zones"]:
+            zone_cols = zone.get("columns", [])
+            zone_cells = zone.get("cells", [])
+            if not zone_cols or not zone_cells:
+                continue
+
+            # Sort columns by x position to determine roles
+            sorted_cols = sorted(zone_cols, key=lambda c: c.get("x_min_px", 0))
+            col_idx_to_pos = {}
+            for pos, col in enumerate(sorted_cols):
+                ci = col.get("col_index", col.get("index", -1))
+                col_idx_to_pos[ci] = pos
+
+            # Skip zones with only 1 column (likely headers/boxes)
+            if len(sorted_cols) < 2:
+                continue
+
+            # Group cells by row
+            rows_map: dict = {}
+            for cell in zone_cells:
+                ri = cell.get("row_index", 0)
+                if ri not in rows_map:
+                    rows_map[ri] = {}
+                ci = cell.get("col_index", 0)
+                rows_map[ri][ci] = (cell.get("text") or "").strip()
+
+            n_cols = len(sorted_cols)
+            for ri in sorted(rows_map.keys()):
+                row = rows_map[ri]
+                # Collect texts in column-position order
+                texts = []
+                for col in sorted_cols:
+                    ci = col.get("col_index", col.get("index", -1))
+                    texts.append(row.get(ci, ""))
+
+                if not any(texts):
+                    continue
+
+                # Map by position, skipping narrow first column (page refs/markers)
+                # Heuristic: if first column is very narrow (<15% of zone width),
+                # it's likely a marker/ref column — skip it for vocab
+                first_col_width = sorted_cols[0].get("x_max_px", 0) - sorted_cols[0].get("x_min_px", 0)
+                zone_width = max(1, (sorted_cols[-1].get("x_max_px", 0) - sorted_cols[0].get("x_min_px", 0)))
+                skip_first = first_col_width / zone_width < 0.15 and n_cols >= 3
+
+                data_texts = texts[1:] if skip_first else texts
+
+                entry = {
+                    "id": str(uuid.uuid4()),
+                    "english": data_texts[0] if len(data_texts) > 0 else "",
+                    "german": data_texts[1] if len(data_texts) > 1 else "",
+                    "example_sentence": " ".join(t for t in data_texts[2:] if t) if len(data_texts) > 2 else "",
+                    "source_page": page_number + 1,
+                }
+                if entry["english"] or entry["german"]:
+                    page_vocabulary.append(entry)
+
+        if page_vocabulary:
+            extraction_source = f"grid-zones ({len(grid_result['zones'])} zones)"
+
+    # B) Fallback: original cells with column classification
+    if not page_vocabulary:
+        col_types = {c.get("type") for c in columns_meta}
+        is_vocab = bool(col_types & {"column_en", "column_de"})
+
+        if is_vocab:
+            entries = _cells_to_vocab_entries(cells, columns_meta)
+            entries = _fix_phonetic_brackets(entries, pronunciation="british")
+            for entry in entries:
+                if not entry.get("english") and not entry.get("german"):
+                    continue
+                page_vocabulary.append({
+                    "id": str(uuid.uuid4()),
+                    "english": entry.get("english", ""),
+                    "german": entry.get("german", ""),
+                    "example_sentence": entry.get("example", ""),
+                    "source_page": page_number + 1,
+                })
+            extraction_source = f"classified ({len(columns_meta)} cols)"
+        else:
+            # Last resort: all cells by position
+            rows_map2: dict = {}
+            for cell in cells:
+                ri = cell.get("row_index", 0)
+                if ri not in rows_map2:
+                    rows_map2[ri] = {}
+                ci = cell.get("col_index", 0)
+                rows_map2[ri][ci] = (cell.get("text") or "").strip()
+            all_ci = sorted({ci for r in rows_map2.values() for ci in r.keys()})
+            for ri in sorted(rows_map2.keys()):
+                row = rows_map2[ri]
+                texts = [row.get(ci, "") for ci in all_ci]
+                if not any(texts):
+                    continue
+                page_vocabulary.append({
+                    "id": str(uuid.uuid4()),
+                    "english": texts[0] if len(texts) > 0 else "",
+                    "german": texts[1] if len(texts) > 1 else "",
+                    "example_sentence": " ".join(texts[2:]) if len(texts) > 2 else "",
+                    "source_page": page_number + 1,
+                })
+            extraction_source = f"generic ({len(all_ci)} cols)"
+
+    # --- Post-processing: merge cell-wrap continuation rows ---
+    if len(page_vocabulary) >= 2:
+        try:
+            # Convert to internal format (example_sentence → example)
+            internal = []
+            for v in page_vocabulary:
+                internal.append({
+                    'row_index': len(internal),
+                    'english': v.get('english', ''),
+                    'german': v.get('german', ''),
+                    'example': v.get('example_sentence', ''),
+                })
+
+            n_before = len(internal)
+            internal = _merge_wrapped_rows(internal)
+            internal = _merge_phonetic_continuation_rows(internal)
+            internal = _merge_continuation_rows(internal)
+
+            if len(internal) < n_before:
+                # Rebuild page_vocabulary from merged entries
+                merged_vocab = []
+                for entry in internal:
+                    if not entry.get('english') and not entry.get('german'):
+                        continue
+                    merged_vocab.append({
+                        'id': str(uuid.uuid4()),
+                        'english': entry.get('english', ''),
+                        'german': entry.get('german', ''),
+                        'example_sentence': entry.get('example', ''),
+                        'source_page': page_number + 1,
+                    })
+                logger.info(f"  row merging: {n_before} → {len(merged_vocab)} entries")
+                page_vocabulary = merged_vocab
+        except Exception as e:
+            logger.warning(f"  row merging failed (non-critical): {e}")
+
+    logger.info(f"  vocab extraction: {len(page_vocabulary)} entries via {extraction_source}")
+
+    total_duration = _time.time() - t_total
+    logger.info(f"Kombi Pipeline page {page_number + 1}: "
+                f"{len(page_vocabulary)} vocab entries in {total_duration:.1f}s")
+
+    return page_vocabulary, rotation, scan_quality_report

klausur-service/backend/vocab/worksheet/upload_api.py

@@ -0,0 +1,490 @@
+"""
+Vocab Worksheet Upload API — PDF upload, thumbnails, and page processing.
+
+Extracted from vocab_worksheet_api.py to keep modules under 500 LOC.
+
+Routes (no prefix — included into the main /api/v1/vocab router):
+  POST /sessions/{session_id}/upload-pdf-info
+  GET  /sessions/{session_id}/pdf-thumbnail/{page_number}
+  GET  /sessions/{session_id}/pdf-page-image/{page_number}
+  POST /sessions/{session_id}/process-single-page/{page_number}
+  POST /sessions/{session_id}/process-pages
+"""
+
+import io
+import logging
+import os
+import uuid
+from typing import List
+
+from fastapi import APIRouter, HTTPException, Query, UploadFile, File
+from fastapi.responses import StreamingResponse
+
+from .models import SessionStatus
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Local storage path
+# ---------------------------------------------------------------------------
+
+LOCAL_STORAGE_PATH = os.getenv("VOCAB_STORAGE_PATH", "/app/vocab-worksheets")
+
+# ---------------------------------------------------------------------------
+# Optional heavy dependencies
+# ---------------------------------------------------------------------------
+
+try:
+    import numpy as np
+    from cv_preprocessing import render_pdf_high_res, detect_and_fix_orientation
+    OCR_PIPELINE_AVAILABLE = True
+except ImportError:
+    np = None  # type: ignore[assignment]
+    OCR_PIPELINE_AVAILABLE = False
+    logger.warning("OCR pipeline imports not available in upload module")
+
+# Sub-module imports (already split out)
+from .generation import (
+    convert_pdf_page_to_image,
+    convert_pdf_to_images,
+    get_pdf_page_count,
+)
+from .extraction import extract_vocabulary_from_image
+
+try:
+    from .ocr import _run_ocr_pipeline_for_page
+except ImportError:
+    _run_ocr_pipeline_for_page = None  # type: ignore[assignment]
+    logger.warning("vocab_worksheet_ocr not available — process-single-page disabled")
+
+# ---------------------------------------------------------------------------
+# In-memory session store (shared with main module)
+# ---------------------------------------------------------------------------
+
+def _get_sessions():
+    from .api import _sessions
+    return _sessions
+
+# ---------------------------------------------------------------------------
+# Router (no prefix — will be included into the main vocab router)
+# ---------------------------------------------------------------------------
+
+upload_router = APIRouter()
+
+
+# =============================================================================
+# POST /sessions/{session_id}/upload-pdf-info
+# =============================================================================
+
+@upload_router.post("/sessions/{session_id}/upload-pdf-info")
+async def upload_pdf_get_info(
+    session_id: str,
+    file: UploadFile = File(...),
+):
+    """
+    Upload a PDF and get page count and thumbnails for preview.
+    Use this before processing to let user select pages.
+    """
+    logger.info(f"PDF info request for session {session_id}")
+
+    if session_id not in _get_sessions():
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    session = _get_sessions()[session_id]
+
+    # Validate file type
+    extension = file.filename.split('.')[-1].lower() if file.filename else ''
+    content_type = file.content_type or ''
+
+    if extension != 'pdf' and content_type != 'application/pdf':
+        raise HTTPException(status_code=400, detail="Only PDF files supported for this endpoint")
+
+    content = await file.read()
+
+    # Save PDF temporarily
+    session_dir = os.path.join(LOCAL_STORAGE_PATH, session_id)
+    os.makedirs(session_dir, exist_ok=True)
+    pdf_path = os.path.join(session_dir, "source.pdf")
+
+    with open(pdf_path, 'wb') as f:
+        f.write(content)
+
+    # Get page count
+    page_count = get_pdf_page_count(content)
+
+    # Store PDF data in session for later processing
+    session["pdf_data"] = content
+    session["pdf_path"] = pdf_path
+    session["pdf_page_count"] = page_count
+    session["status"] = "pdf_uploaded"
+
+    # Detect orientation for each page so thumbnails are shown correctly
+    page_rotations: dict = {}
+    if OCR_PIPELINE_AVAILABLE:
+        for pg in range(page_count):
+            try:
+                img_bgr = render_pdf_high_res(content, pg, zoom=2.0)
+                _, rotation = detect_and_fix_orientation(img_bgr)
+                if rotation:
+                    page_rotations[pg] = rotation
+                    logger.info(f"Page {pg + 1}: orientation {rotation}°")
+            except Exception as e:
+                logger.warning(f"Orientation detection failed for page {pg + 1}: {e}")
+    session["page_rotations"] = page_rotations
+
+    return {
+        "session_id": session_id,
+        "page_count": page_count,
+        "filename": file.filename,
+        "page_rotations": page_rotations,
+    }
+
+
+# =============================================================================
+# GET /sessions/{session_id}/pdf-thumbnail/{page_number}
+# =============================================================================
+
+@upload_router.get("/sessions/{session_id}/pdf-thumbnail/{page_number}")
+async def get_pdf_thumbnail(session_id: str, page_number: int, hires: bool = Query(False)):
+    """Get a thumbnail image of a specific PDF page.
+
+    Uses fitz for rendering so that page_rotations (from OCR orientation
+    detection) are applied consistently.
+
+    Args:
+        hires: If True, return full-resolution image (zoom=2.0) instead of thumbnail (zoom=0.5).
+    """
+    if session_id not in _get_sessions():
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    session = _get_sessions()[session_id]
+    pdf_data = session.get("pdf_data")
+
+    if not pdf_data:
+        raise HTTPException(status_code=400, detail="No PDF uploaded for this session")
+
+    try:
+        import fitz
+        zoom = 2.0 if hires else 0.5
+        pdf_document = fitz.open(stream=pdf_data, filetype="pdf")
+        page = pdf_document[page_number]
+        # Apply orientation correction detected during OCR processing
+        rot = session.get("page_rotations", {}).get(page_number, 0)
+        if rot:
+            page.set_rotation(rot)
+        mat = fitz.Matrix(zoom, zoom)
+        pix = page.get_pixmap(matrix=mat)
+        png_data = pix.tobytes("png")
+        pdf_document.close()
+    except Exception as e:
+        logger.error(f"PDF thumbnail failed: {e}")
+        raise HTTPException(status_code=500, detail=f"PDF rendering failed: {str(e)}")
+
+    return StreamingResponse(
+        io.BytesIO(png_data),
+        media_type="image/png",
+    )
+
+
+# =============================================================================
+# GET /sessions/{session_id}/pdf-page-image/{page_number}
+# =============================================================================
+
+@upload_router.get("/sessions/{session_id}/pdf-page-image/{page_number}")
+async def get_pdf_page_image(session_id: str, page_number: int, zoom: float = Query(2.0, ge=0.5, le=4.0)):
+    """PDF page as PNG at arbitrary resolution (for editor view).
+
+    Args:
+        zoom: Zoom factor (0.5=72DPI, 1.0=144DPI, 2.0=288DPI, 4.0=576DPI).
+    """
+    if session_id not in _get_sessions():
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    session = _get_sessions()[session_id]
+    pdf_data = session.get("pdf_data")
+
+    if not pdf_data:
+        raise HTTPException(status_code=400, detail="No PDF uploaded for this session")
+
+    page_count = session.get("pdf_page_count", 1)
+    if page_number < 0 or page_number >= page_count:
+        raise HTTPException(status_code=400, detail=f"Invalid page number. PDF has {page_count} pages (0-indexed).")
+
+    try:
+        import fitz
+        pdf_document = fitz.open(stream=pdf_data, filetype="pdf")
+        page = pdf_document[page_number]
+        # Apply orientation correction detected during OCR processing
+        rot = session.get("page_rotations", {}).get(page_number, 0)
+        if rot:
+            page.set_rotation(rot)
+        mat = fitz.Matrix(zoom, zoom)
+        pix = page.get_pixmap(matrix=mat)
+        png_data = pix.tobytes("png")
+        pdf_document.close()
+        logger.info(f"PDF page {page_number} rendered at zoom={zoom} rot={rot}: {len(png_data)} bytes")
+    except Exception as e:
+        logger.error(f"PDF page image failed: {e}")
+        raise HTTPException(status_code=500, detail=f"PDF rendering failed: {str(e)}")
+
+    return StreamingResponse(
+        io.BytesIO(png_data),
+        media_type="image/png",
+    )
+
+
+# =============================================================================
+# POST /sessions/{session_id}/process-single-page/{page_number}
+# =============================================================================
+
+@upload_router.post("/sessions/{session_id}/process-single-page/{page_number}")
+async def process_single_page(
+    session_id: str,
+    page_number: int,
+    ipa_mode: str = Query("none", pattern="^(auto|all|de|en|none)$"),
+    syllable_mode: str = Query("none", pattern="^(auto|all|de|en|none)$"),
+    enhance: bool = Query(True, description="Step 3: CLAHE + Denoise for degraded scans"),
+    max_cols: int = Query(3, description="Step 2: Max column count (0=unlimited)"),
+    min_conf: int = Query(0, description="Step 1: Min OCR confidence (0=auto from quality score)"),
+):
+    """
+    Process a SINGLE page of an uploaded PDF using the Kombi OCR pipeline.
+
+    Uses the full Kombi pipeline (orientation -> deskew -> dewarp -> crop ->
+    dual-engine OCR -> grid-build with autocorrect/merge) for best quality.
+
+    Query params:
+        ipa_mode: "none" (default), "auto", "all", "en", "de"
+        syllable_mode: "none" (default), "auto", "all", "en", "de"
+        enhance: true (default) -- apply CLAHE/denoise for degraded scans
+        max_cols: 3 (default) -- max column count (0=unlimited)
+        min_conf: 0 (default=auto) -- min OCR confidence (0=from quality score)
+
+    The frontend should call this sequentially for each page.
+    Returns the vocabulary for just this one page.
+    """
+    logger.info(f"Processing SINGLE page {page_number + 1} for session {session_id}")
+
+    if session_id not in _get_sessions():
+        raise HTTPException(
+            status_code=404,
+            detail="Session nicht im Speicher. Bitte erstellen Sie eine neue Session und laden Sie das PDF erneut hoch.",
+        )
+
+    session = _get_sessions()[session_id]
+    pdf_data = session.get("pdf_data")
+
+    if not pdf_data:
+        raise HTTPException(status_code=400, detail="No PDF uploaded for this session")
+
+    page_count = session.get("pdf_page_count", 1)
+
+    if page_number < 0 or page_number >= page_count:
+        raise HTTPException(status_code=400, detail=f"Invalid page number. PDF has {page_count} pages (0-indexed).")
+
+    # Derive pipeline-level variable names for the quality report
+    enable_enhance = enhance
+    max_columns = max_cols if max_cols > 0 else None
+    override_min_conf = min_conf if min_conf > 0 else None
+
+    # --- OCR Pipeline path (use same render_pdf_high_res as admin OCR pipeline) ---
+    rotation_deg = 0
+    quality_report = None
+    min_ocr_conf = 40  # default; overridden by pipeline when quality report is available
+    if OCR_PIPELINE_AVAILABLE and _run_ocr_pipeline_for_page is not None:
+        try:
+            img_bgr = render_pdf_high_res(pdf_data, page_number, zoom=3.0)
+            page_vocabulary, rotation_deg, quality_report = await _run_ocr_pipeline_for_page(
+                img_bgr, page_number, session_id,
+                ipa_mode=ipa_mode, syllable_mode=syllable_mode,
+                enable_enhance=enable_enhance,
+                max_columns=max_columns,
+                override_min_conf=override_min_conf,
+            )
+            # Update min_ocr_conf from quality report if available
+            if quality_report and hasattr(quality_report, 'recommended_min_conf'):
+                min_ocr_conf = quality_report.recommended_min_conf
+        except Exception as e:
+            logger.error(f"OCR pipeline failed for page {page_number + 1}: {e}", exc_info=True)
+            return {
+                "session_id": session_id,
+                "page_number": page_number + 1,
+                "success": False,
+                "error": f"OCR pipeline error: {e}",
+                "vocabulary": [],
+                "vocabulary_count": 0,
+            }
+    else:
+        # Fallback to LLM vision extraction
+        logger.warning("OCR pipeline not available, falling back to LLM vision")
+        image_data = await convert_pdf_page_to_image(pdf_data, page_number, thumbnail=False)
+        vocabulary, confidence, error = await extract_vocabulary_from_image(
+            image_data,
+            f"page_{page_number + 1}.png",
+            page_number=page_number
+        )
+        if error:
+            logger.warning(f"Page {page_number + 1} failed: {error}")
+            return {
+                "session_id": session_id,
+                "page_number": page_number + 1,
+                "success": False,
+                "error": error,
+                "vocabulary": [],
+                "vocabulary_count": 0,
+            }
+        page_vocabulary = []
+        for entry in vocabulary:
+            entry_dict = entry.dict() if hasattr(entry, 'dict') else (entry.__dict__.copy() if hasattr(entry, '__dict__') else dict(entry))
+            entry_dict['source_page'] = page_number + 1
+            if 'id' not in entry_dict or not entry_dict['id']:
+                entry_dict['id'] = str(uuid.uuid4())
+            page_vocabulary.append(entry_dict)
+
+    logger.info(f"Page {page_number + 1}: {len(page_vocabulary)} Vokabeln extrahiert")
+
+    # Store rotation for this page (used by image/thumbnail endpoints)
+    session.setdefault("page_rotations", {})[page_number] = rotation_deg
+
+    # Add to session's vocabulary (append, don't replace)
+    existing_vocab = session.get("vocabulary", [])
+    # Remove any existing entries from this page (in case of re-processing)
+    existing_vocab = [v for v in existing_vocab if v.get("source_page") != page_number + 1]
+    existing_vocab.extend(page_vocabulary)
+    session["vocabulary"] = existing_vocab
+    session["vocabulary_count"] = len(existing_vocab)
+    session["status"] = SessionStatus.EXTRACTED.value
+
+    result = {
+        "session_id": session_id,
+        "page_number": page_number + 1,
+        "success": True,
+        "vocabulary": page_vocabulary,
+        "vocabulary_count": len(page_vocabulary),
+        "total_vocabulary_count": len(existing_vocab),
+        "extraction_confidence": 0.9,
+        "rotation": rotation_deg,
+    }
+
+    # Add scan quality report + active steps info
+    if quality_report:
+        sq = quality_report.to_dict()
+        sq["active_steps"] = {
+            "step1_confidence": f"min_conf={min_ocr_conf}" if not override_min_conf else f"min_conf={override_min_conf} (override)",
+            "step2_max_columns": f"max_cols={max_columns}" if max_columns else "unlimited",
+            "step3_enhance": "on" if enable_enhance and quality_report.is_degraded else "off",
+        }
+        result["scan_quality"] = sq
+
+    return result
+
+
+# =============================================================================
+# POST /sessions/{session_id}/process-pages  (DEPRECATED)
+# =============================================================================
+
+@upload_router.post("/sessions/{session_id}/process-pages")
+async def process_pdf_pages(
+    session_id: str,
+    pages: List[int] = None,
+    process_all: bool = False,
+):
+    """
+    Process specific pages of an uploaded PDF.
+
+    DEPRECATED: Use /process-single-page/{page_number} instead for better results.
+
+    Args:
+        pages: List of 0-indexed page numbers to process
+        process_all: If True, process all pages
+    """
+    logger.info(f"Process pages request for session {session_id}: pages={pages}, process_all={process_all}")
+
+    if session_id not in _get_sessions():
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    session = _get_sessions()[session_id]
+    pdf_data = session.get("pdf_data")
+
+    if not pdf_data:
+        raise HTTPException(status_code=400, detail="No PDF uploaded for this session")
+
+    page_count = session.get("pdf_page_count", 1)
+
+    # Determine which pages to process
+    if process_all:
+        pages = list(range(page_count))
+    elif pages is None or len(pages) == 0:
+        pages = [0]  # Default to first page
+
+    # Convert selected pages to images
+    images = await convert_pdf_to_images(pdf_data, pages)
+
+    # Extract vocabulary from each page SEQUENTIALLY
+    all_vocabulary = []
+    total_confidence = 0.0
+    successful_pages = []
+    failed_pages = []
+    error_messages = []
+
+    for i, image_data in enumerate(images):
+        page_num = pages[i]
+        logger.info(f"Extracting vocabulary from page {page_num + 1} of {len(images)}...")
+
+        vocabulary, confidence, error = await extract_vocabulary_from_image(
+            image_data,
+            f"page_{page_num + 1}.png",
+            page_number=page_num
+        )
+
+        if error:
+            failed_pages.append(page_num + 1)
+            error_messages.append(error)
+            logger.warning(f"Page {page_num + 1} failed: {error}")
+        else:
+            successful_pages.append(page_num + 1)
+            total_confidence += confidence
+
+            # Add page info to each entry and convert to dict
+            for entry in vocabulary:
+                entry_dict = entry.dict() if hasattr(entry, 'dict') else (entry.__dict__.copy() if hasattr(entry, '__dict__') else dict(entry))
+                entry_dict['source_page'] = page_num + 1
+                all_vocabulary.append(entry_dict)
+
+            logger.info(f"Page {page_num + 1}: {len(vocabulary)} Vokabeln extrahiert")
+
+    avg_confidence = total_confidence / len(successful_pages) if successful_pages else 0
+
+    # Update session
+    session["vocabulary"] = all_vocabulary
+    session["vocabulary_count"] = len(all_vocabulary)
+    session["extraction_confidence"] = avg_confidence
+    session["processed_pages"] = pages
+    session["successful_pages"] = successful_pages
+    session["failed_pages"] = failed_pages
+    session["status"] = SessionStatus.EXTRACTED.value
+
+    # Save first page as preview image
+    if images:
+        session_dir = os.path.join(LOCAL_STORAGE_PATH, session_id)
+        image_path = os.path.join(session_dir, "source.png")
+        with open(image_path, 'wb') as f:
+            f.write(images[0])
+        session["image_path"] = image_path
+
+    result = {
+        "session_id": session_id,
+        "pages_processed": len(pages),
+        "pages_successful": len(successful_pages),
+        "pages_failed": len(failed_pages),
+        "successful_pages": successful_pages,
+        "failed_pages": failed_pages,
+        "vocabulary_count": len(all_vocabulary),
+        "extraction_confidence": avg_confidence,
+        "status": SessionStatus.EXTRACTED.value,
+    }
+
+    if error_messages:
+        result["errors"] = error_messages
+
+    return result

klausur-service/backend/vocab_learn_bridge.py

@@ -1,196 +1,4 @@
-"""
+# Backward-compat shim -- module moved to vocab/learn_bridge.py
-Vocab Learn Bridge — Converts vocabulary session data into Learning Units.
+import importlib as _importlib
-
+import sys as _sys
-Bridges klausur-service (vocab extraction) with backend-lehrer (learning units + generators).
+_sys.modules[__name__] = _importlib.import_module("vocab.learn_bridge")
-Creates a Learning Unit in backend-lehrer, then triggers MC/Cloze/QA generation.
-
-DATENSCHUTZ: All communication stays within Docker network (breakpilot-network).
-"""
-
-import os
-import json
-import logging
-import httpx
-from typing import List, Dict, Any, Optional
-
-logger = logging.getLogger(__name__)
-
-BACKEND_LEHRER_URL = os.getenv("BACKEND_LEHRER_URL", "http://backend-lehrer:8001")
-
-
-def vocab_to_analysis_data(session_name: str, vocabulary: List[Dict[str, Any]]) -> Dict[str, Any]:
-    """
-    Convert vocabulary entries from a vocab session into the analysis_data format
-    expected by backend-lehrer generators (MC, Cloze, QA).
-
-    The generators consume:
-    - title: Display name
-    - subject: Subject area
-    - grade_level: Target grade
-    - canonical_text: Full text representation
-    - printed_blocks: Individual text blocks
-    - vocabulary: Original vocab data (for vocab-specific modules)
-    """
-    canonical_lines = []
-    printed_blocks = []
-
-    for v in vocabulary:
-        en = v.get("english", "").strip()
-        de = v.get("german", "").strip()
-        example = v.get("example_sentence", "").strip()
-
-        if not en and not de:
-            continue
-
-        line = f"{en} = {de}"
-        if example:
-            line += f" ({example})"
-        canonical_lines.append(line)
-
-        block_text = f"{en} — {de}"
-        if example:
-            block_text += f" | {example}"
-        printed_blocks.append({"text": block_text})
-
-    return {
-        "title": session_name,
-        "subject": "English Vocabulary",
-        "grade_level": "5-8",
-        "canonical_text": "\n".join(canonical_lines),
-        "printed_blocks": printed_blocks,
-        "vocabulary": vocabulary,
-    }
-
-
-async def create_learning_unit(
-    session_name: str,
-    vocabulary: List[Dict[str, Any]],
-    grade: Optional[str] = None,
-) -> Dict[str, Any]:
-    """
-    Create a Learning Unit in backend-lehrer from vocabulary data.
-
-    Steps:
-    1. Create unit via POST /api/learning-units/
-    2. Return the created unit info
-
-    Returns dict with unit_id, status, vocabulary_count.
-    """
-    if not vocabulary:
-        raise ValueError("No vocabulary entries provided")
-
-    analysis_data = vocab_to_analysis_data(session_name, vocabulary)
-
-    async with httpx.AsyncClient(timeout=30.0) as client:
-        # 1. Create Learning Unit
-        create_payload = {
-            "title": session_name,
-            "subject": "Englisch",
-            "grade": grade or "5-8",
-        }
-
-        try:
-            resp = await client.post(
-                f"{BACKEND_LEHRER_URL}/api/learning-units/",
-                json=create_payload,
-            )
-            resp.raise_for_status()
-            unit = resp.json()
-        except httpx.HTTPError as e:
-            logger.error(f"Failed to create learning unit: {e}")
-            raise RuntimeError(f"Backend-Lehrer nicht erreichbar: {e}")
-
-        unit_id = unit.get("id")
-        if not unit_id:
-            raise RuntimeError("Learning Unit created but no ID returned")
-
-        logger.info(f"Created learning unit {unit_id} with {len(vocabulary)} vocabulary entries")
-
-        # 2. Save analysis_data as JSON file for generators
-        analysis_dir = os.path.expanduser("~/Arbeitsblaetter/Lerneinheiten")
-        os.makedirs(analysis_dir, exist_ok=True)
-        analysis_path = os.path.join(analysis_dir, f"{unit_id}_analyse.json")
-
-        with open(analysis_path, "w", encoding="utf-8") as f:
-            json.dump(analysis_data, f, ensure_ascii=False, indent=2)
-
-        logger.info(f"Saved analysis data to {analysis_path}")
-
-        return {
-            "unit_id": unit_id,
-            "unit": unit,
-            "analysis_path": analysis_path,
-            "vocabulary_count": len(vocabulary),
-            "status": "created",
-        }
-
-
-async def generate_learning_modules(
-    unit_id: str,
-    analysis_path: str,
-) -> Dict[str, Any]:
-    """
-    Trigger MC, Cloze, and QA generation from analysis data.
-
-    Imports generators directly (they run in-process for klausur-service)
-    or calls backend-lehrer API if generators aren't available locally.
-
-    Returns dict with generation results.
-    """
-    results = {
-        "unit_id": unit_id,
-        "mc": {"status": "pending"},
-        "cloze": {"status": "pending"},
-        "qa": {"status": "pending"},
-    }
-
-    # Load analysis data
-    with open(analysis_path, "r", encoding="utf-8") as f:
-        analysis_data = json.load(f)
-
-    # Try to generate via backend-lehrer API
-    async with httpx.AsyncClient(timeout=120.0) as client:
-        # Generate QA (includes Leitner fields)
-        try:
-            resp = await client.post(
-                f"{BACKEND_LEHRER_URL}/api/learning-units/{unit_id}/generate-qa",
-                json={"analysis_data": analysis_data, "num_questions": min(len(analysis_data.get("vocabulary", [])), 20)},
-            )
-            if resp.status_code == 200:
-                results["qa"] = {"status": "generated", "data": resp.json()}
-            else:
-                logger.warning(f"QA generation returned {resp.status_code}")
-                results["qa"] = {"status": "skipped", "reason": f"HTTP {resp.status_code}"}
-        except Exception as e:
-            logger.warning(f"QA generation failed: {e}")
-            results["qa"] = {"status": "error", "reason": str(e)}
-
-        # Generate MC
-        try:
-            resp = await client.post(
-                f"{BACKEND_LEHRER_URL}/api/learning-units/{unit_id}/generate-mc",
-                json={"analysis_data": analysis_data, "num_questions": min(len(analysis_data.get("vocabulary", [])), 10)},
-            )
-            if resp.status_code == 200:
-                results["mc"] = {"status": "generated", "data": resp.json()}
-            else:
-                results["mc"] = {"status": "skipped", "reason": f"HTTP {resp.status_code}"}
-        except Exception as e:
-            logger.warning(f"MC generation failed: {e}")
-            results["mc"] = {"status": "error", "reason": str(e)}
-
-        # Generate Cloze
-        try:
-            resp = await client.post(
-                f"{BACKEND_LEHRER_URL}/api/learning-units/{unit_id}/generate-cloze",
-                json={"analysis_data": analysis_data},
-            )
-            if resp.status_code == 200:
-                results["cloze"] = {"status": "generated", "data": resp.json()}
-            else:
-                results["cloze"] = {"status": "skipped", "reason": f"HTTP {resp.status_code}"}
-        except Exception as e:
-            logger.warning(f"Cloze generation failed: {e}")
-            results["cloze"] = {"status": "error", "reason": str(e)}
-
-    return results

klausur-service/backend/vocab_session_store.py

@@ -1,428 +1,4 @@
-"""
+# Backward-compat shim -- module moved to vocab/session_store.py
-Vocabulary Session Store - PostgreSQL persistence for vocab extraction sessions.
+import importlib as _importlib
-
+import sys as _sys
-Replaces in-memory storage with database persistence.
+_sys.modules[__name__] = _importlib.import_module("vocab.session_store")
-See migrations/001_vocab_sessions.sql for schema.
-"""
-
-import os
-import uuid
-import logging
-import json
-from typing import Optional, List, Dict, Any
-from datetime import datetime
-
-import asyncpg
-
-logger = logging.getLogger(__name__)
-
-# Database configuration
-DATABASE_URL = os.getenv(
-    "DATABASE_URL",
-    "postgresql://breakpilot:breakpilot@postgres:5432/breakpilot_db"
-)
-
-# Connection pool (initialized lazily)
-_pool: Optional[asyncpg.Pool] = None
-
-
-async def get_pool() -> asyncpg.Pool:
-    """Get or create the database connection pool."""
-    global _pool
-    if _pool is None:
-        _pool = await asyncpg.create_pool(DATABASE_URL, min_size=2, max_size=10)
-    return _pool
-
-
-async def init_vocab_tables():
-    """
-    Initialize vocab tables if they don't exist.
-    This is called at startup.
-    """
-    pool = await get_pool()
-    async with pool.acquire() as conn:
-        # Check if tables exist
-        tables_exist = await conn.fetchval("""
-            SELECT EXISTS (
-                SELECT FROM information_schema.tables
-                WHERE table_name = 'vocab_sessions'
-            )
-        """)
-
-        if not tables_exist:
-            logger.info("Creating vocab tables...")
-            # Read and execute migration
-            migration_path = os.path.join(
-                os.path.dirname(__file__),
-                "migrations/001_vocab_sessions.sql"
-            )
-            if os.path.exists(migration_path):
-                with open(migration_path, "r") as f:
-                    sql = f.read()
-                await conn.execute(sql)
-                logger.info("Vocab tables created successfully")
-            else:
-                logger.warning(f"Migration file not found: {migration_path}")
-        else:
-            logger.debug("Vocab tables already exist")
-
-
-# =============================================================================
-# SESSION OPERATIONS
-# =============================================================================
-
-async def create_session_db(
-    session_id: str,
-    name: str,
-    description: str = "",
-    source_language: str = "en",
-    target_language: str = "de"
-) -> Dict[str, Any]:
-    """Create a new vocabulary session in the database."""
-    pool = await get_pool()
-    async with pool.acquire() as conn:
-        row = await conn.fetchrow("""
-            INSERT INTO vocab_sessions (
-                id, name, description, source_language, target_language,
-                status, vocabulary_count
-            ) VALUES ($1, $2, $3, $4, $5, 'pending', 0)
-            RETURNING *
-        """, uuid.UUID(session_id), name, description, source_language, target_language)
-
-        return _row_to_dict(row)
-
-
-async def get_session_db(session_id: str) -> Optional[Dict[str, Any]]:
-    """Get a session by ID."""
-    pool = await get_pool()
-    async with pool.acquire() as conn:
-        row = await conn.fetchrow("""
-            SELECT * FROM vocab_sessions WHERE id = $1
-        """, uuid.UUID(session_id))
-
-        if row:
-            return _row_to_dict(row)
-        return None
-
-
-async def list_sessions_db(
-    limit: int = 50,
-    offset: int = 0,
-    status: Optional[str] = None
-) -> List[Dict[str, Any]]:
-    """List all sessions with optional filtering."""
-    pool = await get_pool()
-    async with pool.acquire() as conn:
-        if status:
-            rows = await conn.fetch("""
-                SELECT * FROM vocab_sessions
-                WHERE status = $1
-                ORDER BY created_at DESC
-                LIMIT $2 OFFSET $3
-            """, status, limit, offset)
-        else:
-            rows = await conn.fetch("""
-                SELECT * FROM vocab_sessions
-                ORDER BY created_at DESC
-                LIMIT $1 OFFSET $2
-            """, limit, offset)
-
-        return [_row_to_dict(row) for row in rows]
-
-
-async def update_session_db(
-    session_id: str,
-    **kwargs
-) -> Optional[Dict[str, Any]]:
-    """Update a session with given fields."""
-    pool = await get_pool()
-
-    # Build dynamic UPDATE query
-    fields = []
-    values = []
-    param_idx = 1
-
-    allowed_fields = [
-        'name', 'description', 'status', 'vocabulary_count',
-        'extraction_confidence', 'image_path', 'pdf_path', 'pdf_page_count',
-        'ocr_prompts', 'processed_pages', 'successful_pages', 'failed_pages'
-    ]
-
-    for key, value in kwargs.items():
-        if key in allowed_fields:
-            fields.append(f"{key} = ${param_idx}")
-            # Convert dicts/lists to JSON for JSONB columns
-            if key in ['ocr_prompts', 'processed_pages', 'successful_pages', 'failed_pages']:
-                value = json.dumps(value) if value else None
-            values.append(value)
-            param_idx += 1
-
-    if not fields:
-        return await get_session_db(session_id)
-
-    values.append(uuid.UUID(session_id))
-
-    async with pool.acquire() as conn:
-        row = await conn.fetchrow(f"""
-            UPDATE vocab_sessions
-            SET {', '.join(fields)}
-            WHERE id = ${param_idx}
-            RETURNING *
-        """, *values)
-
-        if row:
-            return _row_to_dict(row)
-        return None
-
-
-async def delete_session_db(session_id: str) -> bool:
-    """Delete a session and all related data (cascades)."""
-    pool = await get_pool()
-    async with pool.acquire() as conn:
-        result = await conn.execute("""
-            DELETE FROM vocab_sessions WHERE id = $1
-        """, uuid.UUID(session_id))
-        return result == "DELETE 1"
-
-
-# =============================================================================
-# VOCABULARY OPERATIONS
-# =============================================================================
-
-async def add_vocabulary_db(
-    session_id: str,
-    vocab_list: List[Dict[str, Any]]
-) -> List[Dict[str, Any]]:
-    """Add vocabulary entries to a session."""
-    if not vocab_list:
-        return []
-
-    pool = await get_pool()
-    results = []
-
-    async with pool.acquire() as conn:
-        for vocab in vocab_list:
-            vocab_id = str(uuid.uuid4())
-            row = await conn.fetchrow("""
-                INSERT INTO vocab_entries (
-                    id, session_id, english, german, example_sentence,
-                    example_sentence_gap, word_type, source_page
-                ) VALUES ($1, $2, $3, $4, $5, $6, $7, $8)
-                RETURNING *
-            """,
-                uuid.UUID(vocab_id),
-                uuid.UUID(session_id),
-                vocab.get('english', ''),
-                vocab.get('german', ''),
-                vocab.get('example_sentence'),
-                vocab.get('example_sentence_gap'),
-                vocab.get('word_type'),
-                vocab.get('source_page')
-            )
-            results.append(_row_to_dict(row))
-
-        # Update vocabulary count
-        await conn.execute("""
-            UPDATE vocab_sessions
-            SET vocabulary_count = (
-                SELECT COUNT(*) FROM vocab_entries WHERE session_id = $1
-            )
-            WHERE id = $1
-        """, uuid.UUID(session_id))
-
-    return results
-
-
-async def get_vocabulary_db(
-    session_id: str,
-    source_page: Optional[int] = None
-) -> List[Dict[str, Any]]:
-    """Get vocabulary entries for a session."""
-    pool = await get_pool()
-    async with pool.acquire() as conn:
-        if source_page is not None:
-            rows = await conn.fetch("""
-                SELECT * FROM vocab_entries
-                WHERE session_id = $1 AND source_page = $2
-                ORDER BY created_at
-            """, uuid.UUID(session_id), source_page)
-        else:
-            rows = await conn.fetch("""
-                SELECT * FROM vocab_entries
-                WHERE session_id = $1
-                ORDER BY source_page NULLS LAST, created_at
-            """, uuid.UUID(session_id))
-
-        return [_row_to_dict(row) for row in rows]
-
-
-async def update_vocabulary_db(
-    entry_id: str,
-    **kwargs
-) -> Optional[Dict[str, Any]]:
-    """Update a single vocabulary entry."""
-    pool = await get_pool()
-
-    fields = []
-    values = []
-    param_idx = 1
-
-    allowed_fields = [
-        'english', 'german', 'example_sentence', 'example_sentence_gap',
-        'word_type', 'source_page'
-    ]
-
-    for key, value in kwargs.items():
-        if key in allowed_fields:
-            fields.append(f"{key} = ${param_idx}")
-            values.append(value)
-            param_idx += 1
-
-    if not fields:
-        return None
-
-    values.append(uuid.UUID(entry_id))
-
-    async with pool.acquire() as conn:
-        row = await conn.fetchrow(f"""
-            UPDATE vocab_entries
-            SET {', '.join(fields)}
-            WHERE id = ${param_idx}
-            RETURNING *
-        """, *values)
-
-        if row:
-            return _row_to_dict(row)
-        return None
-
-
-async def clear_page_vocabulary_db(session_id: str, page: int) -> int:
-    """Clear all vocabulary for a specific page."""
-    pool = await get_pool()
-    async with pool.acquire() as conn:
-        result = await conn.execute("""
-            DELETE FROM vocab_entries
-            WHERE session_id = $1 AND source_page = $2
-        """, uuid.UUID(session_id), page)
-
-        # Update vocabulary count
-        await conn.execute("""
-            UPDATE vocab_sessions
-            SET vocabulary_count = (
-                SELECT COUNT(*) FROM vocab_entries WHERE session_id = $1
-            )
-            WHERE id = $1
-        """, uuid.UUID(session_id))
-
-        # Return count of deleted rows
-        count = int(result.split()[-1]) if result else 0
-        return count
-
-
-# =============================================================================
-# WORKSHEET OPERATIONS
-# =============================================================================
-
-async def create_worksheet_db(
-    session_id: str,
-    worksheet_types: List[str],
-    pdf_path: Optional[str] = None,
-    solution_path: Optional[str] = None
-) -> Dict[str, Any]:
-    """Create a worksheet record."""
-    pool = await get_pool()
-    worksheet_id = str(uuid.uuid4())
-
-    async with pool.acquire() as conn:
-        row = await conn.fetchrow("""
-            INSERT INTO vocab_worksheets (
-                id, session_id, worksheet_types, pdf_path, solution_path
-            ) VALUES ($1, $2, $3, $4, $5)
-            RETURNING *
-        """,
-            uuid.UUID(worksheet_id),
-            uuid.UUID(session_id),
-            json.dumps(worksheet_types),
-            pdf_path,
-            solution_path
-        )
-
-        return _row_to_dict(row)
-
-
-async def get_worksheet_db(worksheet_id: str) -> Optional[Dict[str, Any]]:
-    """Get a worksheet by ID."""
-    pool = await get_pool()
-    async with pool.acquire() as conn:
-        row = await conn.fetchrow("""
-            SELECT * FROM vocab_worksheets WHERE id = $1
-        """, uuid.UUID(worksheet_id))
-
-        if row:
-            return _row_to_dict(row)
-        return None
-
-
-async def delete_worksheets_for_session_db(session_id: str) -> int:
-    """Delete all worksheets for a session."""
-    pool = await get_pool()
-    async with pool.acquire() as conn:
-        result = await conn.execute("""
-            DELETE FROM vocab_worksheets WHERE session_id = $1
-        """, uuid.UUID(session_id))
-
-        count = int(result.split()[-1]) if result else 0
-        return count
-
-
-# =============================================================================
-# PDF CACHE OPERATIONS
-# =============================================================================
-
-# Simple in-memory cache for PDF data (temporary until served)
-_pdf_cache: Dict[str, bytes] = {}
-
-
-def cache_pdf_data(worksheet_id: str, pdf_data: bytes) -> None:
-    """Cache PDF data temporarily for download."""
-    _pdf_cache[worksheet_id] = pdf_data
-
-
-def get_cached_pdf_data(worksheet_id: str) -> Optional[bytes]:
-    """Get cached PDF data."""
-    return _pdf_cache.get(worksheet_id)
-
-
-def clear_cached_pdf_data(worksheet_id: str) -> None:
-    """Clear cached PDF data."""
-    _pdf_cache.pop(worksheet_id, None)
-
-
-# =============================================================================
-# HELPER FUNCTIONS
-# =============================================================================
-
-def _row_to_dict(row: asyncpg.Record) -> Dict[str, Any]:
-    """Convert asyncpg Record to dict with proper type handling."""
-    if row is None:
-        return {}
-
-    result = dict(row)
-
-    # Convert UUIDs to strings
-    for key in ['id', 'session_id']:
-        if key in result and result[key] is not None:
-            result[key] = str(result[key])
-
-    # Convert datetimes to ISO strings
-    for key in ['created_at', 'updated_at', 'generated_at']:
-        if key in result and result[key] is not None:
-            result[key] = result[key].isoformat()
-
-    # Parse JSONB fields back to dicts/lists
-    for key in ['ocr_prompts', 'processed_pages', 'successful_pages', 'failed_pages', 'worksheet_types']:
-        if key in result and result[key] is not None:
-            if isinstance(result[key], str):
-                result[key] = json.loads(result[key])
-
-    return result

klausur-service/backend/vocab_worksheet_analysis_api.py

@@ -1,472 +1,4 @@
-"""
+# Backward-compat shim -- module moved to vocab/worksheet/analysis_api.py
-Vocabulary Worksheet Analysis API - OCR export, ground truth labeling,
+import importlib as _importlib
-extract-with-boxes, deskewed images, and learning unit generation.
+import sys as _sys
-
+_sys.modules[__name__] = _importlib.import_module("vocab.worksheet.analysis_api")
-The two large handlers (compare_ocr_methods, analyze_grid) live in
-vocab_worksheet_compare_api.py and are included via compare_router.
-"""
-
-from fastapi import APIRouter, Body, HTTPException
-from fastapi.responses import StreamingResponse
-from pydantic import BaseModel
-from typing import Optional, Dict, Any
-from datetime import datetime
-import os
-import io
-import json
-import logging
-
-def _get_sessions():
-    from vocab_worksheet_api import _sessions
-    return _sessions
-
-def _get_local_storage_path():
-    from vocab_worksheet_api import LOCAL_STORAGE_PATH
-    return LOCAL_STORAGE_PATH
-from vocab_worksheet_generation import convert_pdf_page_to_image
-
-# Try to import Tesseract extractor
-try:
-    from tesseract_vocab_extractor import (
-        extract_bounding_boxes, TESSERACT_AVAILABLE,
-    )
-except ImportError:
-    TESSERACT_AVAILABLE = False
-
-# Try to import Grid Detection Service
-try:
-    from services.grid_detection_service import GridDetectionService
-    GRID_SERVICE_AVAILABLE = True
-except ImportError:
-    GRID_SERVICE_AVAILABLE = False
-
-logger = logging.getLogger(__name__)
-
-analysis_router = APIRouter()
-
-def _ocr_export_dir():
-    return os.path.join(_get_local_storage_path(), "ocr-exports")
-
-def _ground_truth_dir():
-    return os.path.join(_get_local_storage_path(), "ground-truth")
-
-
-# =============================================================================
-# OCR Export Endpoints (for cross-app OCR data sharing)
-# =============================================================================
-
-
-@analysis_router.post("/sessions/{session_id}/ocr-export/{page_number}")
-async def save_ocr_export(session_id: str, page_number: int, data: Dict[str, Any] = Body(...)):
-    """
-    Save OCR export data for cross-app sharing (admin-v2 -> studio-v2).
-
-    Both apps proxy to klausur-service via /klausur-api/, so this endpoint
-    serves as shared storage accessible from both ports.
-    """
-
-    logger.info(f"Saving OCR export for session {session_id}, page {page_number}")
-
-    os.makedirs(_ocr_export_dir(), exist_ok=True)
-
-    # Save the export data
-    export_path = os.path.join(_ocr_export_dir(), f"{session_id}_page{page_number}.json")
-    with open(export_path, 'w', encoding='utf-8') as f:
-        json.dump(data, f, ensure_ascii=False, indent=2)
-
-    # Update latest pointer
-    latest_path = os.path.join(_ocr_export_dir(), "latest.json")
-    with open(latest_path, 'w', encoding='utf-8') as f:
-        json.dump({
-            "session_id": session_id,
-            "page_number": page_number,
-            "saved_at": datetime.utcnow().isoformat(),
-        }, f, ensure_ascii=False, indent=2)
-
-    return {
-        "success": True,
-        "session_id": session_id,
-        "page_number": page_number,
-        "message": "OCR export saved successfully",
-    }
-
-
-@analysis_router.get("/sessions/{session_id}/ocr-export/{page_number}")
-async def load_ocr_export(session_id: str, page_number: int):
-    """Load a specific OCR export by session and page number."""
-
-    export_path = os.path.join(_ocr_export_dir(), f"{session_id}_page{page_number}.json")
-
-    if not os.path.exists(export_path):
-        raise HTTPException(status_code=404, detail="OCR export not found")
-
-    with open(export_path, 'r', encoding='utf-8') as f:
-        data = json.load(f)
-
-    return data
-
-
-@analysis_router.get("/ocr-export/latest")
-async def load_latest_ocr_export():
-    """Load the most recently saved OCR export data."""
-
-    latest_path = os.path.join(_ocr_export_dir(), "latest.json")
-
-    if not os.path.exists(latest_path):
-        raise HTTPException(status_code=404, detail="No OCR exports found")
-
-    with open(latest_path, 'r', encoding='utf-8') as f:
-        pointer = json.load(f)
-
-    session_id = pointer.get("session_id")
-    page_number = pointer.get("page_number")
-
-    export_path = os.path.join(_ocr_export_dir(), f"{session_id}_page{page_number}.json")
-
-    if not os.path.exists(export_path):
-        raise HTTPException(status_code=404, detail="Latest OCR export file not found")
-
-    with open(export_path, 'r', encoding='utf-8') as f:
-        data = json.load(f)
-
-    return data
-
-
-# =============================================================================
-# Extract with Boxes & Deskewed Image
-# =============================================================================
-
-
-async def extract_entries_with_boxes(image_bytes: bytes, lang: str = "eng+deu") -> dict:
-    """Extract vocabulary entries with bounding boxes using Tesseract + GridDetectionService.
-
-    Returns dict with 'entries' list and 'image_width'/'image_height'.
-    Each entry has row_index, english, german, example, confidence, bbox, bbox_en, bbox_de, bbox_ex.
-    All bbox coordinates are in percent (0-100).
-    """
-    if not TESSERACT_AVAILABLE:
-        raise HTTPException(status_code=500, detail="Tesseract not available")
-    if not GRID_SERVICE_AVAILABLE:
-        raise HTTPException(status_code=500, detail="GridDetectionService not available")
-
-    # Step 1: Tesseract word-level bounding boxes
-    tess_result = await extract_bounding_boxes(image_bytes, lang=lang)
-    words = tess_result.get("words", [])
-    img_w = tess_result.get("image_width", 0)
-    img_h = tess_result.get("image_height", 0)
-
-    if not words or img_w == 0 or img_h == 0:
-        return {"entries": [], "image_width": img_w, "image_height": img_h}
-
-    # Step 2: Convert to OCR regions (percentage-based)
-    service = GridDetectionService()
-    regions = service.convert_tesseract_regions(words, img_w, img_h)
-
-    if not regions:
-        return {"entries": [], "image_width": img_w, "image_height": img_h}
-
-    # Step 3: Detect grid
-    grid_result = service.detect_grid(regions)
-
-    if not grid_result.cells:
-        return {"entries": [], "image_width": img_w, "image_height": img_h}
-
-    # Step 4: Group cells by logical_row and column_type
-    from services.grid_detection_service import ColumnType
-
-    entries = []
-    for row_idx, row_cells in enumerate(grid_result.cells):
-        en_text = ""
-        de_text = ""
-        ex_text = ""
-        en_bbox = None
-        de_bbox = None
-        ex_bbox = None
-        row_conf_sum = 0.0
-        row_conf_count = 0
-
-        for cell in row_cells:
-            cell_bbox = {"x": round(cell.x, 2), "y": round(cell.y, 2),
-                         "w": round(cell.width, 2), "h": round(cell.height, 2)}
-
-            if cell.column_type == ColumnType.ENGLISH:
-                en_text = cell.text.strip()
-                en_bbox = cell_bbox
-            elif cell.column_type == ColumnType.GERMAN:
-                de_text = cell.text.strip()
-                de_bbox = cell_bbox
-            elif cell.column_type == ColumnType.EXAMPLE:
-                ex_text = cell.text.strip()
-                ex_bbox = cell_bbox
-
-            if cell.text.strip():
-                row_conf_sum += cell.confidence
-                row_conf_count += 1
-
-        # Skip completely empty rows
-        if not en_text and not de_text and not ex_text:
-            continue
-
-        # Calculate whole-row bounding box
-        all_bboxes = [b for b in [en_bbox, de_bbox, ex_bbox] if b is not None]
-        if all_bboxes:
-            row_x = min(b["x"] for b in all_bboxes)
-            row_y = min(b["y"] for b in all_bboxes)
-            row_right = max(b["x"] + b["w"] for b in all_bboxes)
-            row_bottom = max(b["y"] + b["h"] for b in all_bboxes)
-            row_bbox = {"x": round(row_x, 2), "y": round(row_y, 2),
-                        "w": round(row_right - row_x, 2), "h": round(row_bottom - row_y, 2)}
-        else:
-            row_bbox = {"x": 0, "y": 0, "w": 100, "h": 3}
-
-        avg_conf = round((row_conf_sum / row_conf_count * 100) if row_conf_count > 0 else 0, 1)
-
-        entries.append({
-            "row_index": row_idx,
-            "english": en_text,
-            "german": de_text,
-            "example": ex_text,
-            "confidence": avg_conf,
-            "bbox": row_bbox,
-            "bbox_en": en_bbox or {"x": 0, "y": 0, "w": 0, "h": 0},
-            "bbox_de": de_bbox or {"x": 0, "y": 0, "w": 0, "h": 0},
-            "bbox_ex": ex_bbox or {"x": 0, "y": 0, "w": 0, "h": 0},
-        })
-
-    return {"entries": entries, "image_width": img_w, "image_height": img_h}
-
-
-@analysis_router.post("/sessions/{session_id}/extract-with-boxes/{page_number}")
-async def extract_with_boxes(session_id: str, page_number: int):
-    """Extract vocabulary entries with bounding boxes for ground truth labeling.
-
-    Uses Tesseract + GridDetectionService for spatial positioning.
-    page_number is 0-indexed.
-    """
-    logger.info(f"Extract with boxes for session {session_id}, page {page_number}")
-
-    if session_id not in _get_sessions():
-        raise HTTPException(status_code=404, detail="Session not found")
-
-    session = _get_sessions()[session_id]
-    pdf_data = session.get("pdf_data")
-
-    if not pdf_data:
-        raise HTTPException(status_code=400, detail="No PDF uploaded for this session")
-
-    page_count = session.get("pdf_page_count", 1)
-    if page_number < 0 or page_number >= page_count:
-        raise HTTPException(status_code=400, detail=f"Invalid page number. PDF has {page_count} pages (0-indexed).")
-
-    # Convert page to hires image
-    image_data = await convert_pdf_page_to_image(pdf_data, page_number, thumbnail=False)
-
-    # Deskew image before OCR
-    deskew_angle = 0.0
-    try:
-        from cv_vocab_pipeline import deskew_image_by_word_alignment, CV2_AVAILABLE
-        if CV2_AVAILABLE:
-            image_data, deskew_angle = deskew_image_by_word_alignment(image_data)
-            logger.info(f"Deskew: {deskew_angle:.2f}° for page {page_number}")
-    except Exception as e:
-        logger.warning(f"Deskew failed for page {page_number}: {e}")
-
-    # Cache deskewed image in session for later serving
-    if "deskewed_images" not in session:
-        session["deskewed_images"] = {}
-    session["deskewed_images"][str(page_number)] = image_data
-
-    # Extract entries with boxes (now on deskewed image)
-    result = await extract_entries_with_boxes(image_data)
-
-    # Cache in session
-    if "gt_entries" not in session:
-        session["gt_entries"] = {}
-    session["gt_entries"][str(page_number)] = result["entries"]
-
-    return {
-        "success": True,
-        "entries": result["entries"],
-        "entry_count": len(result["entries"]),
-        "image_width": result["image_width"],
-        "image_height": result["image_height"],
-        "deskew_angle": round(deskew_angle, 2),
-        "deskewed": abs(deskew_angle) > 0.05,
-    }
-
-
-@analysis_router.get("/sessions/{session_id}/deskewed-image/{page_number}")
-async def get_deskewed_image(session_id: str, page_number: int):
-    """Return the deskewed page image as PNG.
-
-    Falls back to the original hires image if no deskewed version is cached.
-    """
-    if session_id not in _get_sessions():
-        raise HTTPException(status_code=404, detail="Session not found")
-
-    session = _get_sessions()[session_id]
-    deskewed = session.get("deskewed_images", {}).get(str(page_number))
-
-    if deskewed:
-        return StreamingResponse(io.BytesIO(deskewed), media_type="image/png")
-
-    # Fallback: render original hires image
-    pdf_data = session.get("pdf_data")
-    if not pdf_data:
-        raise HTTPException(status_code=400, detail="No PDF uploaded for this session")
-
-    image_data = await convert_pdf_page_to_image(pdf_data, page_number, thumbnail=False)
-    return StreamingResponse(io.BytesIO(image_data), media_type="image/png")
-
-
-# =============================================================================
-# Ground Truth Labeling
-# =============================================================================
-
-
-@analysis_router.post("/sessions/{session_id}/ground-truth/{page_number}")
-async def save_ground_truth(session_id: str, page_number: int, data: dict = Body(...)):
-    """Save ground truth labels for a page.
-
-    Expects body with 'entries' list - each entry has english, german, example,
-    status ('confirmed' | 'edited' | 'skipped'), and bbox fields.
-    """
-    logger.info(f"Save ground truth for session {session_id}, page {page_number}")
-
-    if session_id not in _get_sessions():
-        raise HTTPException(status_code=404, detail="Session not found")
-
-    entries = data.get("entries", [])
-    if not entries:
-        raise HTTPException(status_code=400, detail="No entries provided")
-
-    # Save in session
-    session = _get_sessions()[session_id]
-    if "ground_truth" not in session:
-        session["ground_truth"] = {}
-    session["ground_truth"][str(page_number)] = entries
-
-    # Also save to disk
-    os.makedirs(_ground_truth_dir(), exist_ok=True)
-    gt_path = os.path.join(_ground_truth_dir(), f"{session_id}_page{page_number}.json")
-    gt_data = {
-        "session_id": session_id,
-        "page_number": page_number,
-        "saved_at": datetime.now().isoformat(),
-        "entry_count": len(entries),
-        "entries": entries,
-    }
-    with open(gt_path, 'w', encoding='utf-8') as f:
-        json.dump(gt_data, f, ensure_ascii=False, indent=2)
-
-    logger.info(f"Ground truth saved: {len(entries)} entries to {gt_path}")
-
-    confirmed = sum(1 for e in entries if e.get("status") == "confirmed")
-    edited = sum(1 for e in entries if e.get("status") == "edited")
-    skipped = sum(1 for e in entries if e.get("status") == "skipped")
-
-    return {
-        "success": True,
-        "saved_count": len(entries),
-        "confirmed": confirmed,
-        "edited": edited,
-        "skipped": skipped,
-        "file_path": gt_path,
-    }
-
-
-@analysis_router.get("/sessions/{session_id}/ground-truth/{page_number}")
-async def load_ground_truth(session_id: str, page_number: int):
-    """Load saved ground truth for a page."""
-    logger.info(f"Load ground truth for session {session_id}, page {page_number}")
-
-    if session_id not in _get_sessions():
-        raise HTTPException(status_code=404, detail="Session not found")
-
-    # Try session cache first
-    session = _get_sessions()[session_id]
-    cached = session.get("ground_truth", {}).get(str(page_number))
-    if cached:
-        return {"success": True, "entries": cached, "source": "cache"}
-
-    # Try disk
-    gt_path = os.path.join(_ground_truth_dir(), f"{session_id}_page{page_number}.json")
-    if not os.path.exists(gt_path):
-        raise HTTPException(status_code=404, detail="No ground truth found for this page")
-
-    with open(gt_path, 'r', encoding='utf-8') as f:
-        gt_data = json.load(f)
-
-    return {"success": True, "entries": gt_data.get("entries", []), "source": "disk"}
-
-
-# ─── Learning Module Generation ─────────────────────────────────────────────
-
-
-class GenerateLearningUnitRequest(BaseModel):
-    grade: Optional[str] = None
-    generate_modules: bool = True
-
-
-@analysis_router.post("/sessions/{session_id}/generate-learning-unit")
-async def generate_learning_unit_endpoint(session_id: str, request: GenerateLearningUnitRequest = None):
-    """
-    Create a Learning Unit from the vocabulary in this session.
-
-    1. Takes vocabulary from the session
-    2. Creates a Learning Unit in backend-lehrer
-    3. Optionally triggers MC/Cloze/QA generation
-
-    Returns the created unit info and generation status.
-    """
-    if request is None:
-        request = GenerateLearningUnitRequest()
-
-    if session_id not in _get_sessions():
-        raise HTTPException(status_code=404, detail="Session not found")
-
-    session = _get_sessions()[session_id]
-    vocabulary = session.get("vocabulary", [])
-
-    if not vocabulary:
-        raise HTTPException(status_code=400, detail="No vocabulary in this session")
-
-    try:
-        from vocab_learn_bridge import create_learning_unit, generate_learning_modules
-
-        # Step 1: Create Learning Unit
-        result = await create_learning_unit(
-            session_name=session["name"],
-            vocabulary=vocabulary,
-            grade=request.grade,
-        )
-
-        # Step 2: Generate modules if requested
-        if request.generate_modules:
-            try:
-                gen_result = await generate_learning_modules(
-                    unit_id=result["unit_id"],
-                    analysis_path=result["analysis_path"],
-                )
-                result["generation"] = gen_result
-            except Exception as e:
-                logger.warning(f"Module generation failed (unit created): {e}")
-                result["generation"] = {"status": "error", "reason": str(e)}
-
-        return result
-
-    except ImportError:
-        raise HTTPException(status_code=501, detail="vocab_learn_bridge module not available")
-    except ValueError as e:
-        raise HTTPException(status_code=400, detail=str(e))
-    except RuntimeError as e:
-        raise HTTPException(status_code=502, detail=str(e))
-
-
-# =============================================================================
-# Include compare_ocr_methods & analyze_grid from companion module
-# =============================================================================
-
-from vocab_worksheet_compare_api import compare_router  # noqa: E402
-
-analysis_router.include_router(compare_router)

klausur-service/backend/vocab_worksheet_api.py

@@ -1,499 +1,4 @@
-"""
+# Backward-compat shim -- module moved to vocab/worksheet/api.py
-Vocabulary Worksheet API — core CRUD routes for sessions, uploads,
+import importlib as _importlib
-vocabulary editing, worksheet generation, and PDF downloads.
+import sys as _sys
-
+_sys.modules[__name__] = _importlib.import_module("vocab.worksheet.api")
-Sub-routers (included at bottom):
-- vocab_worksheet_upload_api: PDF upload, thumbnails, page processing
-- vocab_worksheet_analysis_api: OCR compare, grid analysis, ground truth
-"""
-
-from fastapi import APIRouter, HTTPException, UploadFile, File, Query
-from fastapi.responses import StreamingResponse
-from typing import List, Dict, Any
-from datetime import datetime
-import uuid
-import os
-import io
-import logging
-
-logger = logging.getLogger(__name__)
-
-# --- Imports from extracted sub-modules ---
-from vocab_worksheet_models import (
-    WorksheetType,
-    SessionStatus,
-    VocabularyEntry,
-    SessionCreate,
-    SessionResponse,
-    VocabularyResponse,
-    VocabularyUpdate,
-    WorksheetGenerateRequest,
-    WorksheetResponse,
-)
-from vocab_worksheet_extraction import extract_vocabulary_from_image
-from vocab_worksheet_generation import (
-    generate_worksheet_html, generate_worksheet_pdf,
-    convert_pdf_page_to_image,
-)
-
-# --- Database integration (used by main.py lifespan) ---
-try:
-    from vocab_session_store import (
-        DATABASE_URL, get_pool, init_vocab_tables,
-        list_sessions_db, get_session_db,
-    )
-except ImportError:
-    DATABASE_URL = os.getenv("DATABASE_URL", "postgresql://breakpilot:breakpilot@postgres:5432/breakpilot_db")
-    get_pool = None
-    init_vocab_tables = None
-    list_sessions_db = None
-    get_session_db = None
-
-_db_pool = None
-
-
-def set_db_pool(pool):
-    """Set the database connection pool (called from main.py lifespan)."""
-    global _db_pool
-    _db_pool = pool
-
-
-async def _init_vocab_table():
-    """Initialize vocab tables in database."""
-    if init_vocab_tables:
-        try:
-            await init_vocab_tables()
-            logger.info("vocab_session_cache table ready")
-        except Exception as e:
-            logger.warning(f"Failed to init vocab tables: {e}")
-    else:
-        logger.info("vocab_session_cache table ready")
-
-
-async def _load_all_sessions():
-    """Load all vocab sessions from database into memory cache."""
-    if not list_sessions_db:
-        logger.info("Loaded 0 vocab sessions from database")
-        return
-
-    try:
-        sessions = await list_sessions_db(limit=500)
-        count = 0
-        for s in sessions:
-            sid = s.get("id") or s.get("session_id")
-            if sid and sid not in _sessions:
-                _sessions[sid] = {
-                    "id": sid,
-                    "name": s.get("name", ""),
-                    "description": s.get("description", ""),
-                    "status": s.get("status", "created"),
-                    "vocabulary_count": s.get("vocabulary_count", 0),
-                    "source_language": s.get("source_language", "en"),
-                    "target_language": s.get("target_language", "de"),
-                    "created_at": str(s.get("created_at", "")),
-                }
-                count += 1
-        logger.info(f"Loaded {count} vocab sessions from database")
-    except Exception as e:
-        logger.warning(f"Failed to load sessions from database: {e}")
-
-
-# --- Router & module-level state ---
-router = APIRouter(prefix="/api/v1/vocab", tags=["Vocabulary Worksheets"])
-LOCAL_STORAGE_PATH = os.getenv("VOCAB_STORAGE_PATH", "/app/vocab-worksheets")
-_sessions: Dict[str, Dict[str, Any]] = {}
-_worksheets: Dict[str, Dict[str, Any]] = {}
-
-
-@router.post("/sessions", response_model=SessionResponse)
-async def create_session(session: SessionCreate):
-    """Create a new vocabulary extraction session."""
-    session_id = str(uuid.uuid4())
-
-    session_data = {
-        "id": session_id,
-        "name": session.name,
-        "description": session.description,
-        "source_language": session.source_language,
-        "target_language": session.target_language,
-        "status": SessionStatus.PENDING.value,
-        "vocabulary": [],
-        "vocabulary_count": 0,
-        "image_path": None,
-        "extraction_confidence": None,
-        "created_at": datetime.utcnow(),
-    }
-
-    _sessions[session_id] = session_data
-
-    # Create storage directory
-    session_dir = os.path.join(LOCAL_STORAGE_PATH, session_id)
-    os.makedirs(session_dir, exist_ok=True)
-
-    return SessionResponse(
-        id=session_id,
-        name=session.name,
-        description=session.description,
-        source_language=session.source_language,
-        target_language=session.target_language,
-        status=SessionStatus.PENDING.value,
-        vocabulary_count=0,
-        image_path=None,
-        created_at=session_data["created_at"],
-    )
-
-
-@router.get("/sessions", response_model=List[SessionResponse])
-async def list_sessions(limit: int = Query(50, ge=1, le=100)):
-    """List all vocabulary sessions."""
-    sessions = sorted(
-        _sessions.values(),
-        key=lambda x: x["created_at"],
-        reverse=True
-    )[:limit]
-
-    return [
-        SessionResponse(
-            id=s["id"],
-            name=s["name"],
-            description=s.get("description"),
-            source_language=s["source_language"],
-            target_language=s["target_language"],
-            status=s["status"],
-            vocabulary_count=s.get("vocabulary_count", 0),
-            image_path=s.get("image_path"),
-            created_at=s["created_at"],
-        )
-        for s in sessions
-    ]
-
-
-@router.get("/sessions/{session_id}", response_model=SessionResponse)
-async def get_session(session_id: str):
-    """Get a specific session."""
-    if session_id not in _sessions:
-        raise HTTPException(status_code=404, detail="Session not found")
-
-    s = _sessions[session_id]
-    return SessionResponse(
-        id=s["id"],
-        name=s["name"],
-        description=s.get("description"),
-        source_language=s["source_language"],
-        target_language=s["target_language"],
-        status=s["status"],
-        vocabulary_count=s.get("vocabulary_count", 0),
-        image_path=s.get("image_path"),
-        created_at=s["created_at"],
-    )
-
-
-@router.post("/sessions/{session_id}/upload")
-async def upload_image(
-    session_id: str,
-    file: UploadFile = File(...),
-):
-    """
-    Upload a textbook page image or PDF and extract vocabulary.
-
-    Supported formats: PNG, JPG, JPEG, PDF
-    """
-    logger.info(f"Upload request for session {session_id}")
-    logger.info(f"File: filename={file.filename}, content_type={file.content_type}")
-
-    if session_id not in _sessions:
-        logger.error(f"Session {session_id} not found")
-        raise HTTPException(status_code=404, detail="Session not found")
-
-    session = _sessions[session_id]
-
-    # Validate file type - check both extension and content type
-    extension = file.filename.split('.')[-1].lower() if file.filename else ''
-    content_type = file.content_type or ''
-
-    # Accept images and PDFs
-    valid_image_extensions = ['png', 'jpg', 'jpeg']
-    valid_image_content_types = ['image/png', 'image/jpeg', 'image/jpg']
-    is_pdf = extension == 'pdf' or content_type == 'application/pdf'
-    is_image = extension in valid_image_extensions or content_type in valid_image_content_types
-
-    if not is_pdf and not is_image:
-        logger.error(f"Invalid file type: extension={extension}, content_type={content_type}")
-        raise HTTPException(
-            status_code=400,
-            detail=f"Only PNG, JPG, JPEG, PDF files are supported. Got: extension={extension}, content_type={content_type}"
-        )
-
-    # Determine final extension for saving
-    if is_pdf:
-        save_extension = 'png'  # PDFs will be converted to PNG
-    elif extension in valid_image_extensions:
-        save_extension = extension
-    elif content_type == 'image/png':
-        save_extension = 'png'
-    else:
-        save_extension = 'jpg'
-
-    # Read file content
-    content = await file.read()
-    logger.info(f"Read {len(content)} bytes from uploaded file")
-
-    # Convert PDF to image if needed
-    if is_pdf:
-        logger.info("Converting PDF to image...")
-        content = await convert_pdf_page_to_image(content, page_number=0)
-        logger.info(f"PDF converted, image size: {len(content)} bytes")
-
-    # Save image
-    session_dir = os.path.join(LOCAL_STORAGE_PATH, session_id)
-    os.makedirs(session_dir, exist_ok=True)
-    image_path = os.path.join(session_dir, f"source.{save_extension}")
-
-    with open(image_path, 'wb') as f:
-        f.write(content)
-
-    # Update session status
-    session["status"] = SessionStatus.PROCESSING.value
-    session["image_path"] = image_path
-
-    # Extract vocabulary using Vision LLM
-    vocabulary, confidence, error = await extract_vocabulary_from_image(content, file.filename or "image.png", page_number=0)
-
-    # Update session with extracted vocabulary
-    session["vocabulary"] = [v.dict() for v in vocabulary]
-    session["vocabulary_count"] = len(vocabulary)
-    session["extraction_confidence"] = confidence
-    session["status"] = SessionStatus.EXTRACTED.value
-
-    result = {
-        "session_id": session_id,
-        "filename": file.filename,
-        "image_path": image_path,
-        "vocabulary_count": len(vocabulary),
-        "extraction_confidence": confidence,
-        "status": SessionStatus.EXTRACTED.value,
-    }
-
-    if error:
-        result["error"] = error
-
-    return result
-
-
-@router.get("/sessions/{session_id}/vocabulary", response_model=VocabularyResponse)
-async def get_vocabulary(session_id: str):
-    """Get extracted vocabulary for a session."""
-    if session_id not in _sessions:
-        raise HTTPException(status_code=404, detail="Session not found")
-    session = _sessions[session_id]
-    vocabulary = [VocabularyEntry(**v) for v in session.get("vocabulary", [])]
-    return VocabularyResponse(
-        session_id=session_id,
-        vocabulary=vocabulary,
-        extraction_confidence=session.get("extraction_confidence"),
-    )
-
-
-@router.put("/sessions/{session_id}/vocabulary")
-async def update_vocabulary(session_id: str, update: VocabularyUpdate):
-    """Update vocabulary entries (for manual corrections)."""
-    if session_id not in _sessions:
-        raise HTTPException(status_code=404, detail="Session not found")
-
-    session = _sessions[session_id]
-    session["vocabulary"] = [v.dict() for v in update.vocabulary]
-    session["vocabulary_count"] = len(update.vocabulary)
-
-    return {
-        "session_id": session_id,
-        "vocabulary_count": len(update.vocabulary),
-        "message": "Vocabulary updated successfully",
-    }
-
-
-@router.post("/sessions/{session_id}/generate", response_model=WorksheetResponse)
-async def generate_worksheet(session_id: str, request: WorksheetGenerateRequest):
-    """Generate worksheet PDF(s) from extracted vocabulary."""
-    if session_id not in _sessions:
-        raise HTTPException(status_code=404, detail="Session not found")
-
-    session = _sessions[session_id]
-    vocabulary = [VocabularyEntry(**v) for v in session.get("vocabulary", [])]
-
-    if not vocabulary:
-        raise HTTPException(status_code=400, detail="No vocabulary to generate worksheet from")
-
-    worksheet_id = str(uuid.uuid4())
-    title = request.title or session["name"]
-
-    # Generate HTML for each worksheet type
-    combined_html = ""
-    for wtype in request.worksheet_types:
-        html = generate_worksheet_html(
-            vocabulary=vocabulary,
-            worksheet_type=wtype,
-            title=f"{title} - {wtype.value}",
-            show_solutions=False,
-            repetitions=request.repetitions,
-            line_height=request.line_height,
-        )
-        combined_html += html + '<div style="page-break-after: always;"></div>'
-
-    # Generate PDF
-    try:
-        pdf_bytes = await generate_worksheet_pdf(combined_html)
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"PDF generation failed: {e}")
-
-    # Save PDF
-    session_dir = os.path.join(LOCAL_STORAGE_PATH, session_id)
-    pdf_path = os.path.join(session_dir, f"worksheet_{worksheet_id}.pdf")
-    with open(pdf_path, 'wb') as f:
-        f.write(pdf_bytes)
-
-    # Generate solution PDF if requested
-    solution_path = None
-    if request.include_solutions:
-        solution_html = ""
-        for wtype in request.worksheet_types:
-            html = generate_worksheet_html(
-                vocabulary=vocabulary,
-                worksheet_type=wtype,
-                title=f"{title} - {wtype.value} (Loesung)",
-                show_solutions=True,
-                repetitions=request.repetitions,
-                line_height=request.line_height,
-            )
-            solution_html += html + '<div style="page-break-after: always;"></div>'
-
-        solution_bytes = await generate_worksheet_pdf(solution_html)
-        solution_path = os.path.join(session_dir, f"solution_{worksheet_id}.pdf")
-        with open(solution_path, 'wb') as f:
-            f.write(solution_bytes)
-
-    # Store worksheet info
-    worksheet_data = {
-        "id": worksheet_id,
-        "session_id": session_id,
-        "worksheet_types": [wt.value for wt in request.worksheet_types],
-        "pdf_path": pdf_path,
-        "solution_path": solution_path,
-        "generated_at": datetime.utcnow(),
-    }
-    _worksheets[worksheet_id] = worksheet_data
-
-    # Update session status
-    session["status"] = SessionStatus.COMPLETED.value
-
-    return WorksheetResponse(
-        id=worksheet_id,
-        session_id=session_id,
-        worksheet_types=worksheet_data["worksheet_types"],
-        pdf_path=pdf_path,
-        solution_path=solution_path,
-        generated_at=worksheet_data["generated_at"],
-    )
-
-
-@router.get("/worksheets/{worksheet_id}/pdf")
-async def download_worksheet_pdf(worksheet_id: str):
-    """Download the generated worksheet PDF."""
-    if worksheet_id not in _worksheets:
-        raise HTTPException(status_code=404, detail="Worksheet not found")
-
-    worksheet = _worksheets[worksheet_id]
-    pdf_path = worksheet["pdf_path"]
-
-    if not os.path.exists(pdf_path):
-        raise HTTPException(status_code=404, detail="PDF file not found")
-
-    with open(pdf_path, 'rb') as f:
-        pdf_bytes = f.read()
-
-    return StreamingResponse(
-        io.BytesIO(pdf_bytes),
-        media_type="application/pdf",
-        headers={"Content-Disposition": f"attachment; filename=worksheet_{worksheet_id}.pdf"}
-    )
-
-
-@router.get("/worksheets/{worksheet_id}/solution")
-async def download_solution_pdf(worksheet_id: str):
-    """Download the solution PDF."""
-    if worksheet_id not in _worksheets:
-        raise HTTPException(status_code=404, detail="Worksheet not found")
-
-    worksheet = _worksheets[worksheet_id]
-    solution_path = worksheet.get("solution_path")
-
-    if not solution_path or not os.path.exists(solution_path):
-        raise HTTPException(status_code=404, detail="Solution PDF not found")
-
-    with open(solution_path, 'rb') as f:
-        pdf_bytes = f.read()
-
-    return StreamingResponse(
-        io.BytesIO(pdf_bytes),
-        media_type="application/pdf",
-        headers={"Content-Disposition": f"attachment; filename=solution_{worksheet_id}.pdf"}
-    )
-
-
-@router.get("/sessions/{session_id}/image")
-async def get_session_image(session_id: str):
-    """Get the uploaded source image for a session."""
-    if session_id not in _sessions:
-        raise HTTPException(status_code=404, detail="Session not found")
-
-    session = _sessions[session_id]
-    image_path = session.get("image_path")
-
-    if not image_path or not os.path.exists(image_path):
-        raise HTTPException(status_code=404, detail="Image not found")
-
-    # Determine content type
-    extension = image_path.split('.')[-1].lower()
-    content_type = {
-        'png': 'image/png',
-        'jpg': 'image/jpeg',
-        'jpeg': 'image/jpeg',
-    }.get(extension, 'application/octet-stream')
-
-    with open(image_path, 'rb') as f:
-        image_bytes = f.read()
-
-    return StreamingResponse(
-        io.BytesIO(image_bytes),
-        media_type=content_type,
-    )
-
-
-@router.delete("/sessions/{session_id}")
-async def delete_session(session_id: str):
-    """Delete a vocabulary session and all associated files."""
-    if session_id not in _sessions:
-        raise HTTPException(status_code=404, detail="Session not found")
-
-    # Delete session directory
-    session_dir = os.path.join(LOCAL_STORAGE_PATH, session_id)
-    if os.path.exists(session_dir):
-        import shutil
-        shutil.rmtree(session_dir)
-
-    # Remove from storage
-    del _sessions[session_id]
-
-    # Remove associated worksheets
-    for wid, ws in list(_worksheets.items()):
-        if ws["session_id"] == session_id:
-            del _worksheets[wid]
-
-    return {"message": "Session deleted successfully", "session_id": session_id}
-
-
-# --- Include sub-routers ---
-from vocab_worksheet_upload_api import upload_router
-from vocab_worksheet_analysis_api import analysis_router
-
-router.include_router(upload_router)
-router.include_router(analysis_router)

klausur-service/backend/vocab_worksheet_compare_api.py

@@ -1,545 +1,4 @@
-"""
+# Backward-compat shim -- module moved to vocab/worksheet/compare_api.py
-Vocabulary Worksheet Compare & Grid Analysis API.
+import importlib as _importlib
-
+import sys as _sys
-Split from vocab_worksheet_analysis_api.py — contains the two largest
+_sys.modules[__name__] = _importlib.import_module("vocab.worksheet.compare_api")
-route handlers: compare_ocr_methods (~234 LOC) and analyze_grid (~255 LOC).
-"""
-
-from fastapi import APIRouter, HTTPException, Query
-from typing import Dict, Any
-import base64
-import json
-import logging
-import os
-
-from vocab_worksheet_extraction import extract_vocabulary_from_image
-
-OLLAMA_URL = os.getenv("OLLAMA_URL", "http://host.docker.internal:11434")
-VISION_MODEL = os.getenv("VISION_MODEL", "llama3.2-vision:11b")
-
-def _get_sessions():
-    from vocab_worksheet_api import _sessions
-    return _sessions
-from vocab_worksheet_generation import convert_pdf_page_to_image
-
-# Try to import Tesseract extractor
-try:
-    from tesseract_vocab_extractor import (
-        run_tesseract_pipeline,
-        match_positions_to_vocab, TESSERACT_AVAILABLE,
-    )
-except ImportError:
-    TESSERACT_AVAILABLE = False
-
-# Try to import CV Pipeline
-try:
-    from cv_vocab_pipeline import run_cv_pipeline, CV_PIPELINE_AVAILABLE
-except ImportError:
-    CV_PIPELINE_AVAILABLE = False
-
-# Try to import Grid Detection Service
-try:
-    from services.grid_detection_service import GridDetectionService
-    GRID_SERVICE_AVAILABLE = True
-except ImportError:
-    GRID_SERVICE_AVAILABLE = False
-
-logger = logging.getLogger(__name__)
-
-compare_router = APIRouter()
-
-
-# =============================================================================
-# OCR Compare & Grid Analysis Endpoints
-# =============================================================================
-
-
-@compare_router.post("/sessions/{session_id}/compare-ocr/{page_number}")
-async def compare_ocr_methods(session_id: str, page_number: int):
-    """
-    Run multiple OCR methods on a page and compare results.
-
-    This endpoint:
-    1. Gets the page image from the session's uploaded PDF
-    2. Runs Vision LLM extraction (primary method)
-    3. Optionally runs Tesseract extraction
-    4. Compares found vocabulary across methods
-    5. Returns structured comparison results
-
-    page_number is 0-indexed.
-    """
-    import httpx
-    import time
-
-    logger.info(f"Compare OCR for session {session_id}, page {page_number}")
-
-    if session_id not in _get_sessions():
-        raise HTTPException(status_code=404, detail="Session not found")
-
-    session = _get_sessions()[session_id]
-    pdf_data = session.get("pdf_data")
-
-    if not pdf_data:
-        raise HTTPException(status_code=400, detail="No PDF uploaded for this session")
-
-    page_count = session.get("pdf_page_count", 1)
-    if page_number < 0 or page_number >= page_count:
-        raise HTTPException(status_code=400, detail=f"Invalid page number. PDF has {page_count} pages (0-indexed).")
-
-    # Convert page to image
-    image_data = await convert_pdf_page_to_image(pdf_data, page_number, thumbnail=False)
-
-    methods_results = {}
-    all_vocab_sets = {}
-
-    # --- Method: Vision LLM ---
-    try:
-        start = time.time()
-        vocab, confidence, error = await extract_vocabulary_from_image(
-            image_data, f"page_{page_number + 1}.png", page_number=page_number, use_hybrid=False
-        )
-        duration = time.time() - start
-
-        vocab_list = []
-        for v in vocab:
-            entry = v.dict() if hasattr(v, 'dict') else (v.__dict__.copy() if hasattr(v, '__dict__') else dict(v))
-            vocab_list.append({
-                "english": entry.get("english", ""),
-                "german": entry.get("german", ""),
-                "example": entry.get("example_sentence", ""),
-            })
-
-        methods_results["vision_llm"] = {
-            "name": "Vision LLM",
-            "model": VISION_MODEL,
-            "duration_seconds": round(duration, 1),
-            "vocabulary_count": len(vocab_list),
-            "vocabulary": vocab_list,
-            "confidence": confidence,
-            "success": len(vocab_list) > 0 and not error,
-            "error": error if error else None,
-        }
-        all_vocab_sets["vision_llm"] = {(v["english"].lower().strip(), v["german"].lower().strip()) for v in vocab_list if v["english"] and v["german"]}
-    except Exception as e:
-        logger.error(f"Vision LLM failed: {e}")
-        methods_results["vision_llm"] = {
-            "name": "Vision LLM",
-            "model": VISION_MODEL,
-            "duration_seconds": 0,
-            "vocabulary_count": 0,
-            "vocabulary": [],
-            "confidence": 0,
-            "success": False,
-            "error": str(e),
-        }
-        all_vocab_sets["vision_llm"] = set()
-
-    # --- Method: Tesseract OCR (bounding boxes + vocab extraction) ---
-    if TESSERACT_AVAILABLE:
-        try:
-            start = time.time()
-            tess_result = await run_tesseract_pipeline(image_data, lang="eng+deu")
-            duration = time.time() - start
-
-            tess_vocab = tess_result.get("vocabulary", [])
-            tess_words = tess_result.get("words", [])
-
-            # Store Tesseract words in session for later use (grid analysis, position matching)
-            session["tesseract_words"] = tess_words
-            session["tesseract_image_width"] = tess_result.get("image_width", 0)
-            session["tesseract_image_height"] = tess_result.get("image_height", 0)
-            session[f"tesseract_page_{page_number}"] = tess_result
-
-            vocab_list_tess = []
-            for v in tess_vocab:
-                vocab_list_tess.append({
-                    "english": v.get("english", ""),
-                    "german": v.get("german", ""),
-                    "example": v.get("example", ""),
-                })
-
-            methods_results["tesseract"] = {
-                "name": "Tesseract OCR",
-                "model": "tesseract-ocr (eng+deu)",
-                "duration_seconds": round(duration, 1),
-                "vocabulary_count": len(vocab_list_tess),
-                "vocabulary": vocab_list_tess,
-                "confidence": 0.7 if tess_vocab else 0,
-                "success": len(vocab_list_tess) > 0,
-                "error": tess_result.get("error"),
-                "word_count": tess_result.get("word_count", 0),
-                "columns_detected": len(tess_result.get("columns", [])),
-            }
-            all_vocab_sets["tesseract"] = {
-                (v["english"].lower().strip(), v["german"].lower().strip())
-                for v in vocab_list_tess if v["english"] and v["german"]
-            }
-
-            # Fuzzy-match: attach Tesseract bounding boxes to Vision LLM results
-            if "vision_llm" in methods_results and methods_results["vision_llm"]["success"]:
-                llm_vocab_with_bbox = match_positions_to_vocab(
-                    tess_words,
-                    methods_results["vision_llm"]["vocabulary"],
-                    tess_result.get("image_width", 1),
-                    tess_result.get("image_height", 1),
-                )
-                methods_results["vision_llm"]["vocabulary"] = llm_vocab_with_bbox
-
-        except Exception as e:
-            logger.error(f"Tesseract failed: {e}")
-            import traceback
-            logger.debug(traceback.format_exc())
-            methods_results["tesseract"] = {
-                "name": "Tesseract OCR",
-                "model": "tesseract-ocr",
-                "duration_seconds": 0,
-                "vocabulary_count": 0,
-                "vocabulary": [],
-                "confidence": 0,
-                "success": False,
-                "error": str(e),
-            }
-            all_vocab_sets["tesseract"] = set()
-
-    # --- Method: CV Pipeline (Document Reconstruction) ---
-    if CV_PIPELINE_AVAILABLE:
-        try:
-            start = time.time()
-            cv_result = await run_cv_pipeline(pdf_data=pdf_data, page_number=page_number)
-            duration = time.time() - start
-
-            cv_vocab = cv_result.vocabulary if not cv_result.error else []
-            vocab_list_cv = []
-            for v in cv_vocab:
-                vocab_list_cv.append({
-                    "english": v.get("english", ""),
-                    "german": v.get("german", ""),
-                    "example": v.get("example", ""),
-                })
-
-            methods_results["cv_pipeline"] = {
-                "name": "CV Pipeline (Document Reconstruction)",
-                "model": "opencv + tesseract (multi-pass)",
-                "duration_seconds": round(duration, 1),
-                "vocabulary_count": len(vocab_list_cv),
-                "vocabulary": vocab_list_cv,
-                "confidence": 0.8 if cv_vocab else 0,
-                "success": len(vocab_list_cv) > 0,
-                "error": cv_result.error,
-                "word_count": cv_result.word_count,
-                "columns_detected": cv_result.columns_detected,
-                "stages": cv_result.stages,
-            }
-            all_vocab_sets["cv_pipeline"] = {
-                (v["english"].lower().strip(), v["german"].lower().strip())
-                for v in vocab_list_cv if v["english"] and v["german"]
-            }
-
-        except Exception as e:
-            logger.error(f"CV Pipeline failed: {e}")
-            import traceback
-            logger.debug(traceback.format_exc())
-            methods_results["cv_pipeline"] = {
-                "name": "CV Pipeline (Document Reconstruction)",
-                "model": "opencv + tesseract (multi-pass)",
-                "duration_seconds": 0,
-                "vocabulary_count": 0,
-                "vocabulary": [],
-                "confidence": 0,
-                "success": False,
-                "error": str(e),
-            }
-            all_vocab_sets["cv_pipeline"] = set()
-
-    # --- Build comparison ---
-    all_unique = set()
-    for vs in all_vocab_sets.values():
-        all_unique |= vs
-
-    found_by_all = []
-    found_by_some = []
-    for english, german in sorted(all_unique):
-        found_in = [m for m, vs in all_vocab_sets.items() if (english, german) in vs]
-        entry = {"english": english, "german": german, "methods": found_in}
-        if len(found_in) == len(all_vocab_sets):
-            found_by_all.append(entry)
-        else:
-            found_by_some.append(entry)
-
-    total_methods = max(len(all_vocab_sets), 1)
-    agreement_rate = len(found_by_all) / max(len(all_unique), 1) if all_unique else 0
-
-    # Find best method
-    best_method = max(all_vocab_sets, key=lambda m: len(all_vocab_sets[m])) if all_vocab_sets else "vision_llm"
-
-    return {
-        "session_id": session_id,
-        "page_number": page_number,
-        "methods": methods_results,
-        "comparison": {
-            "found_by_all_methods": found_by_all,
-            "found_by_some_methods": found_by_some,
-            "total_unique_vocabulary": len(all_unique),
-            "agreement_rate": agreement_rate,
-        },
-        "recommendation": {
-            "best_method": best_method,
-            "reason": f"{len(all_vocab_sets.get(best_method, set()))} Vokabeln erkannt mit hoher Konfidenz",
-        },
-    }
-
-
-@compare_router.post("/sessions/{session_id}/analyze-grid/{page_number}")
-async def analyze_grid(session_id: str, page_number: int, use_tesseract: bool = Query(True)):
-    """
-    Analyze the grid/table structure of a vocabulary page.
-
-    Hybrid approach:
-    1. If Tesseract bounding boxes are available (from compare-ocr), use them for
-       real spatial positions via GridDetectionService.
-    2. Otherwise fall back to Vision LLM for grid structure detection.
-
-    page_number is 0-indexed.
-    Returns GridData structure expected by the frontend GridOverlay component.
-    """
-    import httpx
-    import time
-
-    logger.info(f"Grid analysis for session {session_id}, page {page_number} (use_tesseract={use_tesseract})")
-
-    if session_id not in _get_sessions():
-        raise HTTPException(status_code=404, detail="Session not found")
-
-    session = _get_sessions()[session_id]
-    pdf_data = session.get("pdf_data")
-
-    if not pdf_data:
-        raise HTTPException(status_code=400, detail="No PDF uploaded for this session")
-
-    page_count = session.get("pdf_page_count", 1)
-    if page_number < 0 or page_number >= page_count:
-        raise HTTPException(status_code=400, detail=f"Invalid page number.")
-
-    # Convert page to image
-    image_data = await convert_pdf_page_to_image(pdf_data, page_number, thumbnail=False)
-
-    # --- Strategy 1: Use Tesseract bounding boxes + GridDetectionService ---
-    tess_page_data = session.get(f"tesseract_page_{page_number}")
-
-    if use_tesseract and TESSERACT_AVAILABLE and GRID_SERVICE_AVAILABLE:
-        try:
-            # Run Tesseract if not already cached
-            if not tess_page_data:
-                logger.info("Running Tesseract for grid analysis (not cached)")
-                from tesseract_vocab_extractor import run_tesseract_pipeline as _run_tess
-                tess_page_data = await _run_tess(image_data, lang="eng+deu")
-                session[f"tesseract_page_{page_number}"] = tess_page_data
-                session["tesseract_words"] = tess_page_data.get("words", [])
-                session["tesseract_image_width"] = tess_page_data.get("image_width", 0)
-                session["tesseract_image_height"] = tess_page_data.get("image_height", 0)
-
-            tess_words = tess_page_data.get("words", [])
-            img_w = tess_page_data.get("image_width", 0)
-            img_h = tess_page_data.get("image_height", 0)
-
-            if tess_words and img_w > 0 and img_h > 0:
-                service = GridDetectionService()
-                regions = service.convert_tesseract_regions(tess_words, img_w, img_h)
-
-                if regions:
-                    grid_result = service.detect_grid(regions)
-                    grid_dict = grid_result.to_dict()
-
-                    # Merge LLM text if available (better quality than Tesseract text)
-                    # The LLM vocab was stored during compare-ocr
-                    grid_dict["source"] = "tesseract+grid_service"
-                    grid_dict["word_count"] = len(tess_words)
-
-                    logger.info(f"Tesseract grid: {grid_result.rows}x{grid_result.columns}, "
-                                f"{grid_result.stats.get('recognized', 0)} recognized")
-
-                    return {"success": True, "grid": grid_dict}
-
-            logger.info("Tesseract data insufficient, falling back to LLM")
-
-        except Exception as e:
-            logger.warning(f"Tesseract grid analysis failed, falling back to LLM: {e}")
-            import traceback
-            logger.debug(traceback.format_exc())
-
-    # --- Strategy 2: Fall back to Vision LLM ---
-    image_base64 = base64.b64encode(image_data).decode("utf-8")
-
-    grid_prompt = """Analyze this textbook page image. It contains a vocabulary table/grid.
-
-Your task: Identify the TABLE STRUCTURE and extract each cell's content.
-
-Return a JSON object with this EXACT structure:
-{
-  "rows": <number of rows>,
-  "columns": <number of columns>,
-  "column_types": ["english", "german", "example"],
-  "entries": [
-    {
-      "row": 0,
-      "col": 0,
-      "text": "the word or phrase in this cell",
-      "column_type": "english",
-      "confidence": 0.95
-    }
-  ]
-}
-
-Rules:
-- row and col are 0-indexed
-- column_type is one of: "english", "german", "example", "unknown"
-- Detect whether each column contains English words, German translations, or example sentences
-- Include ALL non-empty cells
-- confidence is 0.0-1.0 based on how clear the text is
-- If a cell is empty, don't include it
-- Return ONLY the JSON, no other text"""
-
-    try:
-        import asyncio
-
-        raw_text = ""
-        max_retries = 3
-        for attempt in range(max_retries):
-            async with httpx.AsyncClient(timeout=300.0) as client:
-                response = await client.post(
-                    f"{OLLAMA_URL}/api/chat",
-                    json={
-                        "model": VISION_MODEL,
-                        "messages": [{"role": "user", "content": grid_prompt, "images": [image_base64]}],
-                        "stream": False,
-                        "options": {"temperature": 0.1, "num_predict": 8192},
-                    },
-                    timeout=300.0,
-                )
-
-            if response.status_code == 500 and attempt < max_retries - 1:
-                wait_time = 10 * (attempt + 1)
-                logger.warning(f"Ollama returned 500, retrying in {wait_time}s (attempt {attempt + 1}/{max_retries})")
-                await asyncio.sleep(wait_time)
-                continue
-            elif response.status_code != 200:
-                error_detail = response.text[:200] if response.text else "Unknown error"
-                return {"success": False, "error": f"Ollama Fehler ({response.status_code}): {error_detail}. Bitte erneut versuchen - evtl. laeuft noch ein anderer OCR-Request."}
-
-            raw_text = response.json().get("message", {}).get("content", "")
-            break
-
-        # Parse JSON from response
-        import re
-        json_match = re.search(r'\{[\s\S]*\}', raw_text)
-        if not json_match:
-            return {"success": False, "error": "Could not parse grid structure from LLM response"}
-
-        grid_raw = json.loads(json_match.group())
-
-        num_rows = grid_raw.get("rows", 0)
-        num_cols = grid_raw.get("columns", 0)
-        column_types = grid_raw.get("column_types", [])
-        entries = grid_raw.get("entries", [])
-
-        if num_rows == 0 or num_cols == 0:
-            return {"success": False, "error": "No grid structure detected"}
-
-        # Ensure column_types has the right length
-        while len(column_types) < num_cols:
-            column_types.append("unknown")
-
-        # Build cell grid with percentage-based coordinates
-        row_height = 100.0 / num_rows
-        col_width = 100.0 / num_cols
-
-        # Track which cells have content
-        cell_map = {}
-        for entry in entries:
-            r = entry.get("row", 0)
-            c = entry.get("col", 0)
-            cell_map[(r, c)] = entry
-
-        cells = []
-        recognized_count = 0
-        empty_count = 0
-        problematic_count = 0
-
-        for r in range(num_rows):
-            row_cells = []
-            for c in range(num_cols):
-                x = c * col_width
-                y = r * row_height
-
-                if (r, c) in cell_map:
-                    entry = cell_map[(r, c)]
-                    text = entry.get("text", "").strip()
-                    conf = entry.get("confidence", 0.8)
-                    col_type = entry.get("column_type", column_types[c] if c < len(column_types) else "unknown")
-
-                    if text:
-                        status = "recognized" if conf >= 0.5 else "problematic"
-                        if status == "recognized":
-                            recognized_count += 1
-                        else:
-                            problematic_count += 1
-                    else:
-                        status = "empty"
-                        empty_count += 1
-                else:
-                    text = ""
-                    conf = 0.0
-                    col_type = column_types[c] if c < len(column_types) else "unknown"
-                    status = "empty"
-                    empty_count += 1
-
-                row_cells.append({
-                    "row": r,
-                    "col": c,
-                    "x": round(x, 2),
-                    "y": round(y, 2),
-                    "width": round(col_width, 2),
-                    "height": round(row_height, 2),
-                    "text": text,
-                    "confidence": conf,
-                    "status": status,
-                    "column_type": col_type,
-                })
-            cells.append(row_cells)
-
-        total = num_rows * num_cols
-        coverage = (recognized_count + problematic_count) / max(total, 1)
-
-        # Column and row boundaries as percentages
-        col_boundaries = [round(c * col_width, 2) for c in range(num_cols + 1)]
-        row_boundaries = [round(r * row_height, 2) for r in range(num_rows + 1)]
-
-        grid_data = {
-            "rows": num_rows,
-            "columns": num_cols,
-            "cells": cells,
-            "column_types": column_types,
-            "column_boundaries": col_boundaries,
-            "row_boundaries": row_boundaries,
-            "deskew_angle": 0.0,
-            "source": "vision_llm",
-            "stats": {
-                "recognized": recognized_count,
-                "problematic": problematic_count,
-                "empty": empty_count,
-                "manual": 0,
-                "total": total,
-                "coverage": round(coverage, 3),
-            },
-        }
-
-        return {"success": True, "grid": grid_data}
-
-    except httpx.TimeoutException:
-        logger.error("Grid analysis timed out")
-        return {"success": False, "error": "Grid-Analyse Timeout (Ollama zu langsam)"}
-    except Exception as e:
-        logger.error(f"Grid analysis failed: {e}")
-        import traceback
-        logger.debug(traceback.format_exc())
-        return {"success": False, "error": f"Grid-Analyse fehlgeschlagen: {str(e)}"}

klausur-service/backend/vocab_worksheet_extraction.py

@@ -1,325 +1,4 @@
-"""Vocabulary extraction from images using Vision LLM and hybrid OCR+LLM.
+# Backward-compat shim -- module moved to vocab/worksheet/extraction.py
-
+import importlib as _importlib
-Contains:
+import sys as _sys
-- VOCAB_EXTRACTION_PROMPT: Prompt template for Vision LLM extraction
+_sys.modules[__name__] = _importlib.import_module("vocab.worksheet.extraction")
-- extract_vocabulary_from_image(): Core extraction (hybrid or Vision LLM)
-- _get_demo_vocabulary(): Demo data for testing
-- parse_vocabulary_json(): Robust JSON parsing with 4-strategy fallback
-"""
-
-import base64
-import json
-import logging
-import os
-import re
-import uuid
-from typing import List
-
-import httpx
-
-from vocab_worksheet_models import VocabularyEntry
-
-logger = logging.getLogger(__name__)
-
-# Ollama Configuration
-OLLAMA_URL = os.getenv("OLLAMA_URL", "http://host.docker.internal:11434")
-VISION_MODEL = os.getenv("OLLAMA_VISION_MODEL", "qwen2.5vl:32b")
-
-
-# =============================================================================
-# Vision LLM Vocabulary Extraction
-# =============================================================================
-
-VOCAB_EXTRACTION_PROMPT = """Analysiere dieses Bild einer Vokabelliste aus einem Schulbuch.
-
-AUFGABE: Extrahiere alle Vokabeleintraege in folgendem JSON-Format:
-
-{
-  "vocabulary": [
-    {
-      "english": "to improve",
-      "german": "verbessern",
-      "example": "I want to improve my English."
-    }
-  ]
-}
-
-REGELN:
-1. Erkenne das typische 3-Spalten-Layout: Englisch | Deutsch | Beispielsatz
-2. Behalte die exakte Schreibweise bei
-3. Bei fehlenden Beispielsaetzen: "example": null
-4. Ignoriere Seitenzahlen, Ueberschriften, Kapitelnummern
-5. Gib NUR valides JSON zurueck, keine Erklaerungen
-6. Wenn Wortarten angegeben sind (n, v, adj), extrahiere sie als "word_type"
-
-Beispiel-Output:
-{
-  "vocabulary": [
-    {"english": "achievement", "german": "Leistung, Errungenschaft", "example": "Her achievements were impressive.", "word_type": "n"},
-    {"english": "to achieve", "german": "erreichen, erzielen", "example": "She achieved her goals.", "word_type": "v"}
-  ]
-}"""
-
-
-async def extract_vocabulary_from_image(
-    image_data: bytes,
-    filename: str,
-    page_number: int = 0,
-    use_hybrid: bool = False  # DISABLED: PaddleOCR crashes on ARM64 Mac Mini
-) -> tuple[List[VocabularyEntry], float, str]:
-    """
-    Extract vocabulary from an image using hybrid OCR+LLM or Vision LLM (default).
-
-    Args:
-        image_data: Image bytes
-        filename: Original filename for logging
-        page_number: 0-indexed page number for error messages
-        use_hybrid: If True, use PaddleOCR + LLM (faster, more accurate for printed text)
-                   If False, use Vision LLM (slower, better for complex layouts)
-
-    Returns:
-        Tuple of (vocabulary_entries, confidence, error_message)
-        error_message is empty string on success
-    """
-
-    # ==========================================================================
-    # HYBRID APPROACH (Default): PaddleOCR + LLM Gateway
-    # ==========================================================================
-    if use_hybrid:
-        try:
-            from hybrid_vocab_extractor import extract_vocabulary_hybrid
-            logger.info(f"Using HYBRID extraction for {filename} (PaddleOCR + LLM)")
-
-            vocab_dicts, confidence, error = await extract_vocabulary_hybrid(image_data, page_number)
-
-            if error:
-                logger.warning(f"Hybrid extraction had issues: {error}")
-                # Fall through to Vision LLM fallback
-            elif vocab_dicts:
-                # Convert dicts to VocabularyEntry objects
-                vocabulary = [
-                    VocabularyEntry(
-                        id=str(uuid.uuid4()),
-                        english=v.get("english", ""),
-                        german=v.get("german", ""),
-                        example_sentence=v.get("example"),
-                        source_page=page_number + 1
-                    )
-                    for v in vocab_dicts
-                    if v.get("english") and v.get("german")
-                ]
-                logger.info(f"Hybrid extraction: {len(vocabulary)} entries from {filename}")
-                return vocabulary, confidence, ""
-
-        except ImportError as e:
-            logger.warning(f"Hybrid extractor not available: {e}. Falling back to Vision LLM.")
-        except Exception as e:
-            logger.warning(f"Hybrid extraction failed: {e}. Falling back to Vision LLM.")
-            import traceback
-            logger.debug(traceback.format_exc())
-
-    # ==========================================================================
-    # FALLBACK: Vision LLM (Ollama llama3.2-vision)
-    # ==========================================================================
-    logger.info(f"Using VISION LLM extraction for {filename}")
-
-    try:
-        # First check if Ollama is available
-        async with httpx.AsyncClient(timeout=10.0) as check_client:
-            try:
-                health_response = await check_client.get(f"{OLLAMA_URL}/api/tags")
-                if health_response.status_code != 200:
-                    logger.error(f"Ollama not available at {OLLAMA_URL}")
-                    return [], 0.0, f"Seite {page_number + 1}: Ollama nicht verfuegbar"
-            except Exception as e:
-                logger.error(f"Ollama health check failed: {e}")
-                return [], 0.0, f"Seite {page_number + 1}: Verbindung zu Ollama fehlgeschlagen"
-
-        image_base64 = base64.b64encode(image_data).decode("utf-8")
-
-        payload = {
-            "model": VISION_MODEL,
-            "messages": [
-                {
-                    "role": "user",
-                    "content": VOCAB_EXTRACTION_PROMPT,
-                    "images": [image_base64]
-                }
-            ],
-            "stream": False,
-            "options": {
-                "temperature": 0.1,
-                "num_predict": 4096,
-            }
-        }
-
-        logger.info(f"Extracting vocabulary from {filename} ({len(image_data)} bytes) using {VISION_MODEL}")
-
-        # Increased timeout for Vision models (they can be slow)
-        async with httpx.AsyncClient(timeout=600.0) as client:
-            response = await client.post(
-                f"{OLLAMA_URL}/api/chat",
-                json=payload,
-                timeout=300.0  # 5 minutes per page
-            )
-            response.raise_for_status()
-
-            data = response.json()
-            extracted_text = data.get("message", {}).get("content", "")
-
-        logger.info(f"Ollama response received: {len(extracted_text)} chars")
-
-        # Parse JSON from response
-        vocabulary = parse_vocabulary_json(extracted_text)
-
-        # Set source_page for each entry
-        for v in vocabulary:
-            v.source_page = page_number + 1
-
-        # Estimate confidence
-        confidence = 0.85 if len(vocabulary) > 0 else 0.1
-
-        logger.info(f"Vision LLM extracted {len(vocabulary)} vocabulary entries from {filename}")
-
-        return vocabulary, confidence, ""
-
-    except httpx.TimeoutException:
-        logger.error(f"Ollama request timed out for {filename} (model: {VISION_MODEL})")
-        return [], 0.0, f"Seite {page_number + 1}: Timeout - Verarbeitung dauerte zu lange"
-    except Exception as e:
-        logger.error(f"Vocabulary extraction failed for {filename}: {e}")
-        import traceback
-        logger.error(traceback.format_exc())
-        return [], 0.0, f"Seite {page_number + 1}: Fehler - {str(e)[:50]}"
-
-
-def _get_demo_vocabulary() -> List[VocabularyEntry]:
-    """Return demo vocabulary for testing when Vision LLM is not available."""
-    demo_entries = [
-        {"english": "to achieve", "german": "erreichen, erzielen", "example": "She achieved her goals."},
-        {"english": "achievement", "german": "Leistung, Errungenschaft", "example": "That was a great achievement."},
-        {"english": "improve", "german": "verbessern", "example": "I want to improve my English."},
-        {"english": "improvement", "german": "Verbesserung", "example": "There has been a lot of improvement."},
-        {"english": "success", "german": "Erfolg", "example": "The project was a success."},
-        {"english": "successful", "german": "erfolgreich", "example": "She is a successful businesswoman."},
-        {"english": "fail", "german": "scheitern, durchfallen", "example": "Don't be afraid to fail."},
-        {"english": "failure", "german": "Misserfolg, Versagen", "example": "Failure is part of learning."},
-    ]
-    return [
-        VocabularyEntry(
-            id=str(uuid.uuid4()),
-            english=e["english"],
-            german=e["german"],
-            example_sentence=e.get("example"),
-        )
-        for e in demo_entries
-    ]
-
-
-def parse_vocabulary_json(text: str) -> List[VocabularyEntry]:
-    """Parse vocabulary JSON from LLM response with robust error handling."""
-
-    def clean_json_string(s: str) -> str:
-        """Clean a JSON string by removing control characters and fixing common issues."""
-        # Remove control characters except newlines and tabs
-        s = re.sub(r'[\x00-\x08\x0b\x0c\x0e-\x1f]', '', s)
-        # Replace unescaped newlines within strings with space
-        # This is a simplistic approach - replace actual newlines with escaped ones
-        s = s.replace('\n', '\\n').replace('\r', '\\r').replace('\t', '\\t')
-        return s
-
-    def try_parse_json(json_str: str) -> dict:
-        """Try multiple strategies to parse JSON."""
-        # Strategy 1: Direct parse
-        try:
-            return json.loads(json_str)
-        except json.JSONDecodeError:
-            pass
-
-        # Strategy 2: Clean and parse
-        try:
-            cleaned = clean_json_string(json_str)
-            return json.loads(cleaned)
-        except json.JSONDecodeError:
-            pass
-
-        # Strategy 3: Try to fix common issues
-        try:
-            # Remove trailing commas before } or ]
-            fixed = re.sub(r',(\s*[}\]])', r'\1', json_str)
-            # Fix unquoted keys
-            fixed = re.sub(r'(\{|\,)\s*([a-zA-Z_][a-zA-Z0-9_]*)\s*:', r'\1"\2":', fixed)
-            return json.loads(fixed)
-        except json.JSONDecodeError:
-            pass
-
-        return None
-
-    try:
-        # Find JSON in response (may have extra text)
-        start = text.find('{')
-        end = text.rfind('}') + 1
-
-        if start == -1 or end == 0:
-            logger.warning("No JSON found in response")
-            return []
-
-        json_str = text[start:end]
-        data = try_parse_json(json_str)
-
-        if data is None:
-            # Strategy 4: Extract vocabulary entries using regex as fallback
-            logger.warning("JSON parsing failed, trying regex extraction")
-            vocabulary = []
-            # Match patterns like {"english": "...", "german": "...", ...}
-            pattern = r'\{\s*"english"\s*:\s*"([^"]*?)"\s*,\s*"german"\s*:\s*"([^"]*?)"(?:\s*,\s*"example"\s*:\s*(?:"([^"]*?)"|null))?'
-            matches = re.findall(pattern, text, re.IGNORECASE | re.DOTALL)
-
-            for match in matches:
-                english = match[0].strip() if match[0] else ""
-                german = match[1].strip() if match[1] else ""
-                example = match[2].strip() if len(match) > 2 and match[2] else None
-
-                if english and german:
-                    vocab_entry = VocabularyEntry(
-                        id=str(uuid.uuid4()),
-                        english=english,
-                        german=german,
-                        example_sentence=example,
-                    )
-                    vocabulary.append(vocab_entry)
-
-            if vocabulary:
-                logger.info(f"Regex extraction found {len(vocabulary)} entries")
-            return vocabulary
-
-        # Normal JSON parsing succeeded
-        vocabulary = []
-        for i, entry in enumerate(data.get("vocabulary", [])):
-            english = entry.get("english", "").strip()
-            german = entry.get("german", "").strip()
-
-            # Skip entries that look like hallucinations (very long or containing unusual patterns)
-            if len(english) > 100 or len(german) > 200:
-                logger.warning(f"Skipping suspicious entry: {english[:50]}...")
-                continue
-
-            if not english or not german:
-                continue
-
-            vocab_entry = VocabularyEntry(
-                id=str(uuid.uuid4()),
-                english=english,
-                german=german,
-                example_sentence=entry.get("example"),
-                word_type=entry.get("word_type"),
-            )
-            vocabulary.append(vocab_entry)
-
-        return vocabulary
-
-    except Exception as e:
-        logger.error(f"Failed to parse vocabulary JSON: {e}")
-        import traceback
-        logger.error(traceback.format_exc())
-        return []

klausur-service/backend/vocab_worksheet_generation.py

@@ -1,260 +1,4 @@
-"""
+# Backward-compat shim -- module moved to vocab/worksheet/generation.py
-Vocabulary Worksheet Generation — HTML/PDF generation and PDF utilities.
+import importlib as _importlib
-
+import sys as _sys
-Extracted from vocab_worksheet_api.py to keep modules under 500 LOC.
+_sys.modules[__name__] = _importlib.import_module("vocab.worksheet.generation")
-
-Functions:
-  - generate_worksheet_html(): Build HTML for various worksheet types
-  - generate_worksheet_pdf():  Convert HTML to PDF via WeasyPrint
-  - get_pdf_page_count():      Count pages in a PDF (PyMuPDF)
-  - convert_pdf_page_to_image(): Render single PDF page to PNG
-  - convert_pdf_to_images():     Render multiple PDF pages to PNG
-"""
-
-import io
-import logging
-import os
-from typing import List, Optional
-
-from fastapi import HTTPException
-
-from vocab_worksheet_models import VocabularyEntry, WorksheetType
-
-logger = logging.getLogger(__name__)
-
-# Optional dependency: WeasyPrint
-try:
-    from weasyprint import HTML as _WeasyHTML
-    WEASYPRINT_AVAILABLE = True
-except (ImportError, OSError):
-    WEASYPRINT_AVAILABLE = False
-    logger.warning("WeasyPrint not available")
-
-# Optional dependency: PyMuPDF
-try:
-    import fitz  # PyMuPDF
-    FITZ_AVAILABLE = True
-except ImportError:
-    FITZ_AVAILABLE = False
-    logger.warning("PyMuPDF (fitz) not available")
-
-
-# =============================================================================
-# Worksheet HTML Generation
-# =============================================================================
-
-def generate_worksheet_html(
-    vocabulary: List[VocabularyEntry],
-    worksheet_type: WorksheetType,
-    title: str,
-    show_solutions: bool = False,
-    repetitions: int = 3,
-    line_height: str = "normal"
-) -> str:
-    """Generate HTML for a worksheet."""
-
-    # Line height CSS
-    line_heights = {
-        "normal": "2.5em",
-        "large": "3.5em",
-        "extra-large": "4.5em"
-    }
-    lh = line_heights.get(line_height, "2.5em")
-
-    html = f"""<!DOCTYPE html>
-<html>
-<head>
-    <meta charset="UTF-8">
-    <style>
-        @page {{ size: A4; margin: 2cm; }}
-        body {{ font-family: 'Segoe UI', Arial, sans-serif; font-size: 14px; }}
-        h1 {{ font-size: 24px; margin-bottom: 10px; }}
-        .meta {{ color: #666; margin-bottom: 20px; }}
-        .name-line {{ margin-bottom: 30px; }}
-        .vocab-table {{ width: 100%; border-collapse: collapse; }}
-        .vocab-table td {{ padding: 8px; border-bottom: 1px solid #ddd; line-height: {lh}; }}
-        .vocab-word {{ width: 40%; font-weight: 500; }}
-        .vocab-blank {{ width: 60%; border-bottom: 2px dotted #999; }}
-        .vocab-answer {{ width: 60%; color: #2563eb; }}
-        .gap {{ border-bottom: 2px solid #333; min-width: 100px; display: inline-block; }}
-        .hint {{ color: #666; font-style: italic; font-size: 12px; }}
-        .section {{ margin-top: 30px; }}
-        .section-title {{ font-size: 16px; font-weight: 600; margin-bottom: 15px; color: #374151; }}
-    </style>
-</head>
-<body>
-    <h1>{title}</h1>
-    <div class="name-line">Name: _________________________ Datum: _____________</div>
-"""
-
-    if worksheet_type == WorksheetType.EN_TO_DE:
-        html += '<div class="section"><div class="section-title">Uebersetze ins Deutsche:</div>'
-        html += '<table class="vocab-table">'
-        for entry in vocabulary:
-            if show_solutions:
-                html += f'<tr><td class="vocab-word">{entry.english}</td><td class="vocab-answer">{entry.german}</td></tr>'
-            else:
-                html += f'<tr><td class="vocab-word">{entry.english}</td><td class="vocab-blank"></td></tr>'
-        html += '</table></div>'
-
-    elif worksheet_type == WorksheetType.DE_TO_EN:
-        html += '<div class="section"><div class="section-title">Uebersetze ins Englische:</div>'
-        html += '<table class="vocab-table">'
-        for entry in vocabulary:
-            if show_solutions:
-                html += f'<tr><td class="vocab-word">{entry.german}</td><td class="vocab-answer">{entry.english}</td></tr>'
-            else:
-                html += f'<tr><td class="vocab-word">{entry.german}</td><td class="vocab-blank"></td></tr>'
-        html += '</table></div>'
-
-    elif worksheet_type == WorksheetType.COPY_PRACTICE:
-        html += '<div class="section"><div class="section-title">Schreibe jedes Wort mehrmals:</div>'
-        html += '<table class="vocab-table">'
-        for entry in vocabulary:
-            html += f'<tr><td class="vocab-word">{entry.english}</td>'
-            html += '<td class="vocab-blank">'
-            if show_solutions:
-                html += f' {entry.english} ' * repetitions
-            html += '</td></tr>'
-        html += '</table></div>'
-
-    elif worksheet_type == WorksheetType.GAP_FILL:
-        entries_with_examples = [e for e in vocabulary if e.example_sentence]
-        if entries_with_examples:
-            html += '<div class="section"><div class="section-title">Fuege das passende Wort ein:</div>'
-            for i, entry in enumerate(entries_with_examples, 1):
-                # Create gap sentence by removing the English word
-                gap_sentence = entry.example_sentence
-                for word in entry.english.split():
-                    if word.lower() in gap_sentence.lower():
-                        gap_sentence = gap_sentence.replace(word, '<span class="gap"></span>')
-                        gap_sentence = gap_sentence.replace(word.capitalize(), '<span class="gap"></span>')
-                        gap_sentence = gap_sentence.replace(word.lower(), '<span class="gap"></span>')
-                        break
-
-                html += f'<p>{i}. {gap_sentence}</p>'
-                if show_solutions:
-                    html += f'<p class="hint">Loesung: {entry.english}</p>'
-                else:
-                    html += f'<p class="hint">({entry.german})</p>'
-            html += '</div>'
-
-    html += '</body></html>'
-    return html
-
-
-# =============================================================================
-# Worksheet PDF Generation
-# =============================================================================
-
-async def generate_worksheet_pdf(html: str) -> bytes:
-    """Generate PDF from HTML using WeasyPrint."""
-    try:
-        from weasyprint import HTML
-        pdf_bytes = HTML(string=html).write_pdf()
-        return pdf_bytes
-    except ImportError:
-        logger.warning("WeasyPrint not available, returning HTML")
-        return html.encode('utf-8')
-    except Exception as e:
-        logger.error(f"PDF generation failed: {e}")
-        raise
-
-
-# =============================================================================
-# PDF Utilities (PyMuPDF)
-# =============================================================================
-
-def get_pdf_page_count(pdf_data: bytes) -> int:
-    """Get the number of pages in a PDF."""
-    try:
-        import fitz
-        pdf_document = fitz.open(stream=pdf_data, filetype="pdf")
-        count = pdf_document.page_count
-        pdf_document.close()
-        return count
-    except Exception as e:
-        logger.error(f"Failed to get PDF page count: {e}")
-        return 0
-
-
-async def convert_pdf_page_to_image(pdf_data: bytes, page_number: int = 0, thumbnail: bool = False) -> bytes:
-    """Convert a specific page of PDF to PNG image using PyMuPDF.
-
-    Args:
-        pdf_data: PDF file as bytes
-        page_number: 0-indexed page number
-        thumbnail: If True, return a smaller thumbnail image
-    """
-    try:
-        import fitz  # PyMuPDF
-
-        pdf_document = fitz.open(stream=pdf_data, filetype="pdf")
-
-        if pdf_document.page_count == 0:
-            raise ValueError("PDF has no pages")
-
-        if page_number >= pdf_document.page_count:
-            raise ValueError(f"Page {page_number} does not exist (PDF has {pdf_document.page_count} pages)")
-
-        page = pdf_document[page_number]
-
-        # Render page to image
-        # For thumbnails: lower resolution, for OCR: higher resolution
-        zoom = 0.5 if thumbnail else 2.0
-        mat = fitz.Matrix(zoom, zoom)
-        pix = page.get_pixmap(matrix=mat)
-
-        png_data = pix.tobytes("png")
-        pdf_document.close()
-
-        logger.info(f"Converted PDF page {page_number} to PNG: {len(png_data)} bytes (thumbnail={thumbnail})")
-        return png_data
-
-    except ImportError:
-        logger.error("PyMuPDF (fitz) not installed")
-        raise HTTPException(status_code=500, detail="PDF conversion not available - PyMuPDF not installed")
-    except Exception as e:
-        logger.error(f"PDF conversion failed: {e}")
-        raise HTTPException(status_code=400, detail=f"PDF conversion failed: {str(e)}")
-
-
-async def convert_pdf_to_images(pdf_data: bytes, pages: List[int] = None) -> List[bytes]:
-    """Convert multiple pages of PDF to PNG images.
-
-    Args:
-        pdf_data: PDF file as bytes
-        pages: List of 0-indexed page numbers to convert. If None, convert all pages.
-    """
-    try:
-        import fitz
-
-        pdf_document = fitz.open(stream=pdf_data, filetype="pdf")
-
-        if pdf_document.page_count == 0:
-            raise ValueError("PDF has no pages")
-
-        # If no pages specified, convert all
-        if pages is None:
-            pages = list(range(pdf_document.page_count))
-
-        images = []
-        zoom = 2.0
-        mat = fitz.Matrix(zoom, zoom)
-
-        for page_num in pages:
-            if page_num < pdf_document.page_count:
-                page = pdf_document[page_num]
-                pix = page.get_pixmap(matrix=mat)
-                images.append(pix.tobytes("png"))
-
-        pdf_document.close()
-        logger.info(f"Converted {len(images)} PDF pages to images")
-        return images
-
-    except ImportError:
-        logger.error("PyMuPDF (fitz) not installed")
-        raise HTTPException(status_code=500, detail="PDF conversion not available")
-    except Exception as e:
-        logger.error(f"PDF conversion failed: {e}")
-        raise HTTPException(status_code=400, detail=f"PDF conversion failed: {str(e)}")

klausur-service/backend/vocab_worksheet_models.py

@@ -1,86 +1,4 @@
-"""Pydantic models and enums for the Vocab Worksheet API."""
+# Backward-compat shim -- module moved to vocab/worksheet/models.py
-
+import importlib as _importlib
-from datetime import datetime
+import sys as _sys
-from enum import Enum
+_sys.modules[__name__] = _importlib.import_module("vocab.worksheet.models")
-from typing import List, Optional
-
-from pydantic import BaseModel
-
-
-# =============================================================================
-# Enums
-# =============================================================================
-
-class WorksheetType(str, Enum):
-    EN_TO_DE = "en_to_de"          # English -> German translation
-    DE_TO_EN = "de_to_en"          # German -> English translation
-    COPY_PRACTICE = "copy"         # Write word multiple times
-    GAP_FILL = "gap_fill"          # Fill in the blanks
-    COMBINED = "combined"          # All types combined
-
-
-class SessionStatus(str, Enum):
-    PENDING = "pending"            # Session created, no upload yet
-    PROCESSING = "processing"      # OCR in progress
-    EXTRACTED = "extracted"        # Vocabulary extracted, ready to edit
-    COMPLETED = "completed"        # Worksheet generated
-
-
-# =============================================================================
-# Pydantic Models
-# =============================================================================
-
-class VocabularyEntry(BaseModel):
-    id: str
-    english: str
-    german: str
-    example_sentence: Optional[str] = None
-    example_sentence_gap: Optional[str] = None  # With ___ for gap-fill
-    word_type: Optional[str] = None  # noun, verb, adjective, etc.
-    source_page: Optional[int] = None  # Page number where entry was found (1-indexed)
-
-
-class SessionCreate(BaseModel):
-    name: str
-    description: Optional[str] = None
-    source_language: str = "en"  # Source language (default English)
-    target_language: str = "de"  # Target language (default German)
-
-
-class SessionResponse(BaseModel):
-    id: str
-    name: str
-    description: Optional[str]
-    source_language: str
-    target_language: str
-    status: str
-    vocabulary_count: int
-    image_path: Optional[str]
-    created_at: datetime
-
-
-class VocabularyResponse(BaseModel):
-    session_id: str
-    vocabulary: List[VocabularyEntry]
-    extraction_confidence: Optional[float]
-
-
-class VocabularyUpdate(BaseModel):
-    vocabulary: List[VocabularyEntry]
-
-
-class WorksheetGenerateRequest(BaseModel):
-    worksheet_types: List[WorksheetType]
-    title: Optional[str] = None
-    include_solutions: bool = True
-    repetitions: int = 3  # For copy practice
-    line_height: str = "normal"  # normal, large, extra-large
-
-
-class WorksheetResponse(BaseModel):
-    id: str
-    session_id: str
-    worksheet_types: List[str]
-    pdf_path: str
-    solution_path: Optional[str]
-    generated_at: datetime

klausur-service/backend/vocab_worksheet_ocr.py

@@ -1,481 +1,4 @@
-"""
+# Backward-compat shim -- module moved to vocab/worksheet/ocr.py
-Vocab Worksheet OCR Pipeline — full Kombi OCR pipeline for a single page.
+import importlib as _importlib
-
+import sys as _sys
-Extracted from vocab_worksheet_api.py to keep file sizes manageable.
+_sys.modules[__name__] = _importlib.import_module("vocab.worksheet.ocr")
-
-Pipeline steps:
-  orientation → deskew → dewarp → crop → scan-quality → enhance →
-  dual-engine OCR (RapidOCR + Tesseract) → merge → grid-build →
-  vocab extraction → row merging
-"""
-
-import logging
-import uuid
-from typing import Optional
-
-logger = logging.getLogger(__name__)
-
-# ---------------------------------------------------------------------------
-# Optional heavy dependencies (not available in every environment)
-# ---------------------------------------------------------------------------
-
-try:
-    import cv2
-    import numpy as np
-except ImportError:
-    cv2 = None  # type: ignore[assignment]
-    np = None  # type: ignore[assignment]
-    logger.warning("cv2 / numpy not available — OCR pipeline disabled")
-
-try:
-    from PIL import Image
-except ImportError:
-    Image = None  # type: ignore[assignment]
-
-try:
-    import pytesseract
-except ImportError:
-    pytesseract = None  # type: ignore[assignment]
-
-# CV pipeline helpers
-try:
-    from cv_vocab_pipeline import (
-        deskew_two_pass,
-        dewarp_image,
-        detect_and_fix_orientation,
-        _cells_to_vocab_entries,
-        _fix_phonetic_brackets,
-    )
-except ImportError:
-    deskew_two_pass = None  # type: ignore[assignment]
-    dewarp_image = None  # type: ignore[assignment]
-    detect_and_fix_orientation = None  # type: ignore[assignment]
-    _cells_to_vocab_entries = None  # type: ignore[assignment]
-    _fix_phonetic_brackets = None  # type: ignore[assignment]
-
-try:
-    from cv_cell_grid import (
-        _merge_wrapped_rows,
-        _merge_phonetic_continuation_rows,
-        _merge_continuation_rows,
-    )
-except ImportError:
-    _merge_wrapped_rows = None  # type: ignore[assignment]
-    _merge_phonetic_continuation_rows = None  # type: ignore[assignment]
-    _merge_continuation_rows = None  # type: ignore[assignment]
-
-try:
-    from cv_ocr_engines import ocr_region_rapid
-except ImportError:
-    ocr_region_rapid = None  # type: ignore[assignment]
-
-try:
-    from cv_vocab_types import PageRegion
-except ImportError:
-    PageRegion = None  # type: ignore[assignment]
-
-try:
-    from ocr_pipeline_ocr_merge import (
-        _split_paddle_multi_words,
-        _merge_paddle_tesseract,
-        _deduplicate_words,
-    )
-except ImportError:
-    _split_paddle_multi_words = None  # type: ignore[assignment]
-    _merge_paddle_tesseract = None  # type: ignore[assignment]
-    _deduplicate_words = None  # type: ignore[assignment]
-
-try:
-    from cv_words_first import build_grid_from_words
-except ImportError:
-    build_grid_from_words = None  # type: ignore[assignment]
-
-try:
-    from ocr_pipeline_session_store import (
-        create_session_db as create_pipeline_session_db,
-        update_session_db as update_pipeline_session_db,
-    )
-except ImportError:
-    create_pipeline_session_db = None  # type: ignore[assignment]
-    update_pipeline_session_db = None  # type: ignore[assignment]
-
-
-# ---------------------------------------------------------------------------
-# Main pipeline function
-# ---------------------------------------------------------------------------
-
-async def _run_ocr_pipeline_for_page(
-    img_bgr: "np.ndarray",
-    page_number: int,
-    vocab_session_id: str,
-    *,
-    ipa_mode: str = "none",
-    syllable_mode: str = "none",
-    enable_enhance: bool = True,
-    max_columns: Optional[int] = 3,
-    override_min_conf: Optional[int] = None,
-) -> tuple:
-    """Run the full Kombi OCR pipeline on a single page and return vocab entries.
-
-    Uses the same pipeline as the admin OCR Kombi pipeline:
-    orientation → deskew → dewarp → crop → dual-engine OCR → grid-build
-    (with pipe-autocorrect, word-gap merge, dictionary detection, etc.)
-
-    Args:
-        img_bgr: BGR numpy array.
-        page_number: 0-indexed page number.
-        vocab_session_id: Vocab session ID for logging.
-        ipa_mode: "none" (default for worksheets), "auto", "all", "en", "de".
-        syllable_mode: "none" (default for worksheets), "auto", "all", "en", "de".
-
-    Returns (entries, rotation_deg) where entries is a list of dicts and
-    rotation_deg is the orientation correction applied (0, 90, 180, 270).
-    """
-    import time as _time
-
-    t_total = _time.time()
-    img_h, img_w = img_bgr.shape[:2]
-    logger.info(f"Kombi Pipeline page {page_number + 1}: image {img_w}x{img_h}")
-
-    # 1. Orientation detection (fix upside-down scans)
-    t0 = _time.time()
-    img_bgr, rotation = detect_and_fix_orientation(img_bgr)
-    if rotation:
-        img_h, img_w = img_bgr.shape[:2]
-        logger.info(f"  orientation: rotated {rotation}° ({_time.time() - t0:.1f}s)")
-    else:
-        logger.info(f"  orientation: OK ({_time.time() - t0:.1f}s)")
-
-    # 2. Create pipeline session in DB (visible in admin Kombi UI)
-    pipeline_session_id = str(uuid.uuid4())
-    try:
-        _, png_buf = cv2.imencode(".png", img_bgr)
-        original_png = png_buf.tobytes()
-        await create_pipeline_session_db(
-            pipeline_session_id,
-            name=f"vocab-ws-{vocab_session_id[:8]}-p{page_number + 1}",
-            filename=f"page_{page_number + 1}.png",
-            original_png=original_png,
-        )
-    except Exception as e:
-        logger.warning(f"Could not create pipeline session in DB: {e}")
-
-    # 3. Three-pass deskew
-    t0 = _time.time()
-    deskewed_bgr, angle_applied, deskew_debug = deskew_two_pass(img_bgr.copy())
-    logger.info(f"  deskew: angle={angle_applied:.2f} ({_time.time() - t0:.1f}s)")
-
-    # 4. Dewarp
-    t0 = _time.time()
-    dewarped_bgr, dewarp_info = dewarp_image(deskewed_bgr)
-    logger.info(f"  dewarp: shear={dewarp_info['shear_degrees']:.3f} ({_time.time() - t0:.1f}s)")
-
-    # 5. Content crop (removes scanner borders, gutter shadows)
-    t0 = _time.time()
-    try:
-        from page_crop import detect_and_crop_page
-        cropped_bgr, crop_result = detect_and_crop_page(dewarped_bgr)
-        if crop_result.get("crop_applied"):
-            dewarped_bgr = cropped_bgr
-            logger.info(f"  crop: applied ({_time.time() - t0:.1f}s)")
-        else:
-            logger.info(f"  crop: skipped ({_time.time() - t0:.1f}s)")
-    except Exception as e:
-        logger.warning(f"  crop: failed ({e}), continuing with uncropped image")
-
-    # 5b. Scan quality assessment
-    scan_quality_report = None
-    try:
-        from scan_quality import score_scan_quality
-        scan_quality_report = score_scan_quality(dewarped_bgr)
-    except Exception as e:
-        logger.warning(f"  scan quality: failed ({e})")
-
-    if override_min_conf:
-        min_ocr_conf = override_min_conf
-    else:
-        min_ocr_conf = scan_quality_report.recommended_min_conf if scan_quality_report else 40
-
-    # 5c. Image enhancement for degraded scans
-    is_degraded = scan_quality_report.is_degraded if scan_quality_report else False
-    if is_degraded and enable_enhance:
-        try:
-            from ocr_image_enhance import enhance_for_ocr
-            dewarped_bgr = enhance_for_ocr(dewarped_bgr, is_degraded=True)
-            logger.info("  enhancement: applied (degraded scan)")
-        except Exception as e:
-            logger.warning(f"  enhancement: failed ({e})")
-
-    # 6. Dual-engine OCR (RapidOCR + Tesseract → merge)
-    t0 = _time.time()
-    img_h, img_w = dewarped_bgr.shape[:2]
-
-    # RapidOCR (local ONNX)
-    try:
-        from cv_ocr_engines import ocr_region_rapid
-        from cv_vocab_types import PageRegion
-        full_region = PageRegion(type="full_page", x=0, y=0, width=img_w, height=img_h)
-        rapid_words = ocr_region_rapid(dewarped_bgr, full_region) or []
-    except Exception as e:
-        logger.warning(f"  RapidOCR failed: {e}")
-        rapid_words = []
-
-    # Tesseract
-    from PIL import Image
-    import pytesseract
-    pil_img = Image.fromarray(cv2.cvtColor(dewarped_bgr, cv2.COLOR_BGR2RGB))
-    data = pytesseract.image_to_data(
-        pil_img, lang="eng+deu", config="--psm 6 --oem 3",
-        output_type=pytesseract.Output.DICT,
-    )
-    tess_words = []
-    for i in range(len(data["text"])):
-        text = str(data["text"][i]).strip()
-        conf_raw = str(data["conf"][i])
-        conf = int(conf_raw) if conf_raw.lstrip("-").isdigit() else -1
-        if not text or conf < min_ocr_conf:
-            continue
-        tess_words.append({
-            "text": text,
-            "left": data["left"][i], "top": data["top"][i],
-            "width": data["width"][i], "height": data["height"][i],
-            "conf": conf,
-        })
-
-    # Merge dual-engine results
-    from ocr_pipeline_ocr_merge import _split_paddle_multi_words, _merge_paddle_tesseract, _deduplicate_words
-    from cv_words_first import build_grid_from_words
-
-    rapid_split = _split_paddle_multi_words(rapid_words) if rapid_words else []
-    if rapid_split or tess_words:
-        merged_words = _merge_paddle_tesseract(rapid_split, tess_words)
-        merged_words = _deduplicate_words(merged_words)
-    else:
-        merged_words = tess_words  # fallback to Tesseract only
-
-    # Build initial grid from merged words
-    cells, columns_meta = build_grid_from_words(merged_words, img_w, img_h, max_columns=max_columns)
-    for cell in cells:
-        cell["ocr_engine"] = "rapid_kombi"
-
-    n_rows = len(set(c["row_index"] for c in cells)) if cells else 0
-    n_cols = len(columns_meta)
-    logger.info(f"  ocr: rapid={len(rapid_words)}, tess={len(tess_words)}, "
-                f"merged={len(merged_words)}, cells={len(cells)} ({_time.time() - t0:.1f}s)")
-
-    # 7. Save word_result to pipeline session (needed by _build_grid_core)
-    word_result = {
-        "cells": cells,
-        "grid_shape": {"rows": n_rows, "cols": n_cols, "total_cells": len(cells)},
-        "columns_used": columns_meta,
-        "layout": "vocab" if {c.get("type") for c in columns_meta} & {"column_en", "column_de"} else "generic",
-        "image_width": img_w,
-        "image_height": img_h,
-        "duration_seconds": 0,
-        "ocr_engine": "rapid_kombi",
-        "raw_tesseract_words": tess_words,
-        "summary": {
-            "total_cells": len(cells),
-            "non_empty_cells": sum(1 for c in cells if c.get("text")),
-        },
-    }
-
-    # Save images + word_result to pipeline session for admin visibility
-    try:
-        _, dsk_buf = cv2.imencode(".png", deskewed_bgr)
-        _, dwp_buf = cv2.imencode(".png", dewarped_bgr)
-        await update_pipeline_session_db(
-            pipeline_session_id,
-            deskewed_png=dsk_buf.tobytes(),
-            dewarped_png=dwp_buf.tobytes(),
-            cropped_png=cv2.imencode(".png", dewarped_bgr)[1].tobytes(),
-            word_result=word_result,
-            deskew_result={"angle_applied": round(angle_applied, 3)},
-            dewarp_result={"shear_degrees": dewarp_info.get("shear_degrees", 0)},
-            current_step=8,
-        )
-    except Exception as e:
-        logger.warning(f"Could not update pipeline session: {e}")
-
-    # 8. Run full grid-build (with pipe-autocorrect, word-gap merge, etc.)
-    t0 = _time.time()
-    try:
-        from grid_editor_api import _build_grid_core
-        session_data = {
-            "word_result": word_result,
-        }
-        grid_result = await _build_grid_core(
-            pipeline_session_id, session_data,
-            ipa_mode=ipa_mode, syllable_mode=syllable_mode,
-        )
-        logger.info(f"  grid-build: {grid_result.get('summary', {}).get('total_cells', 0)} cells "
-                    f"({_time.time() - t0:.1f}s)")
-
-        # Save grid result to pipeline session
-        try:
-            await update_pipeline_session_db(
-                pipeline_session_id,
-                grid_editor_result=grid_result,
-                current_step=11,
-            )
-        except Exception:
-            pass
-
-    except Exception as e:
-        logger.warning(f"  grid-build failed: {e}, falling back to basic grid")
-        grid_result = None
-
-    # 9. Extract vocab entries
-    # Prefer grid-build result (better column detection, more cells) over
-    # the initial build_grid_from_words() which often under-clusters.
-    page_vocabulary = []
-    extraction_source = "none"
-
-    # A) Try grid-build zones first (best quality: 4-column detection, autocorrect)
-    if grid_result and grid_result.get("zones"):
-        for zone in grid_result["zones"]:
-            zone_cols = zone.get("columns", [])
-            zone_cells = zone.get("cells", [])
-            if not zone_cols or not zone_cells:
-                continue
-
-            # Sort columns by x position to determine roles
-            sorted_cols = sorted(zone_cols, key=lambda c: c.get("x_min_px", 0))
-            col_idx_to_pos = {}
-            for pos, col in enumerate(sorted_cols):
-                ci = col.get("col_index", col.get("index", -1))
-                col_idx_to_pos[ci] = pos
-
-            # Skip zones with only 1 column (likely headers/boxes)
-            if len(sorted_cols) < 2:
-                continue
-
-            # Group cells by row
-            rows_map: dict = {}
-            for cell in zone_cells:
-                ri = cell.get("row_index", 0)
-                if ri not in rows_map:
-                    rows_map[ri] = {}
-                ci = cell.get("col_index", 0)
-                rows_map[ri][ci] = (cell.get("text") or "").strip()
-
-            n_cols = len(sorted_cols)
-            for ri in sorted(rows_map.keys()):
-                row = rows_map[ri]
-                # Collect texts in column-position order
-                texts = []
-                for col in sorted_cols:
-                    ci = col.get("col_index", col.get("index", -1))
-                    texts.append(row.get(ci, ""))
-
-                if not any(texts):
-                    continue
-
-                # Map by position, skipping narrow first column (page refs/markers)
-                # Heuristic: if first column is very narrow (<15% of zone width),
-                # it's likely a marker/ref column — skip it for vocab
-                first_col_width = sorted_cols[0].get("x_max_px", 0) - sorted_cols[0].get("x_min_px", 0)
-                zone_width = max(1, (sorted_cols[-1].get("x_max_px", 0) - sorted_cols[0].get("x_min_px", 0)))
-                skip_first = first_col_width / zone_width < 0.15 and n_cols >= 3
-
-                data_texts = texts[1:] if skip_first else texts
-
-                entry = {
-                    "id": str(uuid.uuid4()),
-                    "english": data_texts[0] if len(data_texts) > 0 else "",
-                    "german": data_texts[1] if len(data_texts) > 1 else "",
-                    "example_sentence": " ".join(t for t in data_texts[2:] if t) if len(data_texts) > 2 else "",
-                    "source_page": page_number + 1,
-                }
-                if entry["english"] or entry["german"]:
-                    page_vocabulary.append(entry)
-
-        if page_vocabulary:
-            extraction_source = f"grid-zones ({len(grid_result['zones'])} zones)"
-
-    # B) Fallback: original cells with column classification
-    if not page_vocabulary:
-        col_types = {c.get("type") for c in columns_meta}
-        is_vocab = bool(col_types & {"column_en", "column_de"})
-
-        if is_vocab:
-            entries = _cells_to_vocab_entries(cells, columns_meta)
-            entries = _fix_phonetic_brackets(entries, pronunciation="british")
-            for entry in entries:
-                if not entry.get("english") and not entry.get("german"):
-                    continue
-                page_vocabulary.append({
-                    "id": str(uuid.uuid4()),
-                    "english": entry.get("english", ""),
-                    "german": entry.get("german", ""),
-                    "example_sentence": entry.get("example", ""),
-                    "source_page": page_number + 1,
-                })
-            extraction_source = f"classified ({len(columns_meta)} cols)"
-        else:
-            # Last resort: all cells by position
-            rows_map2: dict = {}
-            for cell in cells:
-                ri = cell.get("row_index", 0)
-                if ri not in rows_map2:
-                    rows_map2[ri] = {}
-                ci = cell.get("col_index", 0)
-                rows_map2[ri][ci] = (cell.get("text") or "").strip()
-            all_ci = sorted({ci for r in rows_map2.values() for ci in r.keys()})
-            for ri in sorted(rows_map2.keys()):
-                row = rows_map2[ri]
-                texts = [row.get(ci, "") for ci in all_ci]
-                if not any(texts):
-                    continue
-                page_vocabulary.append({
-                    "id": str(uuid.uuid4()),
-                    "english": texts[0] if len(texts) > 0 else "",
-                    "german": texts[1] if len(texts) > 1 else "",
-                    "example_sentence": " ".join(texts[2:]) if len(texts) > 2 else "",
-                    "source_page": page_number + 1,
-                })
-            extraction_source = f"generic ({len(all_ci)} cols)"
-
-    # --- Post-processing: merge cell-wrap continuation rows ---
-    if len(page_vocabulary) >= 2:
-        try:
-            # Convert to internal format (example_sentence → example)
-            internal = []
-            for v in page_vocabulary:
-                internal.append({
-                    'row_index': len(internal),
-                    'english': v.get('english', ''),
-                    'german': v.get('german', ''),
-                    'example': v.get('example_sentence', ''),
-                })
-
-            n_before = len(internal)
-            internal = _merge_wrapped_rows(internal)
-            internal = _merge_phonetic_continuation_rows(internal)
-            internal = _merge_continuation_rows(internal)
-
-            if len(internal) < n_before:
-                # Rebuild page_vocabulary from merged entries
-                merged_vocab = []
-                for entry in internal:
-                    if not entry.get('english') and not entry.get('german'):
-                        continue
-                    merged_vocab.append({
-                        'id': str(uuid.uuid4()),
-                        'english': entry.get('english', ''),
-                        'german': entry.get('german', ''),
-                        'example_sentence': entry.get('example', ''),
-                        'source_page': page_number + 1,
-                    })
-                logger.info(f"  row merging: {n_before} → {len(merged_vocab)} entries")
-                page_vocabulary = merged_vocab
-        except Exception as e:
-            logger.warning(f"  row merging failed (non-critical): {e}")
-
-    logger.info(f"  vocab extraction: {len(page_vocabulary)} entries via {extraction_source}")
-
-    total_duration = _time.time() - t_total
-    logger.info(f"Kombi Pipeline page {page_number + 1}: "
-                f"{len(page_vocabulary)} vocab entries in {total_duration:.1f}s")
-
-    return page_vocabulary, rotation, scan_quality_report

klausur-service/backend/vocab_worksheet_upload_api.py

@@ -1,490 +1,4 @@
-"""
+# Backward-compat shim -- module moved to vocab/worksheet/upload_api.py
-Vocab Worksheet Upload API — PDF upload, thumbnails, and page processing.
+import importlib as _importlib
-
+import sys as _sys
-Extracted from vocab_worksheet_api.py to keep modules under 500 LOC.
+_sys.modules[__name__] = _importlib.import_module("vocab.worksheet.upload_api")
-
-Routes (no prefix — included into the main /api/v1/vocab router):
-  POST /sessions/{session_id}/upload-pdf-info
-  GET  /sessions/{session_id}/pdf-thumbnail/{page_number}
-  GET  /sessions/{session_id}/pdf-page-image/{page_number}
-  POST /sessions/{session_id}/process-single-page/{page_number}
-  POST /sessions/{session_id}/process-pages
-"""
-
-import io
-import logging
-import os
-import uuid
-from typing import List, Optional
-
-from fastapi import APIRouter, HTTPException, Query, UploadFile, File
-from fastapi.responses import StreamingResponse
-
-from vocab_worksheet_models import SessionStatus, VocabularyEntry
-
-logger = logging.getLogger(__name__)
-
-# ---------------------------------------------------------------------------
-# Local storage path
-# ---------------------------------------------------------------------------
-
-LOCAL_STORAGE_PATH = os.getenv("VOCAB_STORAGE_PATH", "/app/vocab-worksheets")
-
-# ---------------------------------------------------------------------------
-# Optional heavy dependencies
-# ---------------------------------------------------------------------------
-
-try:
-    import numpy as np
-    from cv_preprocessing import render_pdf_high_res, detect_and_fix_orientation
-    OCR_PIPELINE_AVAILABLE = True
-except ImportError:
-    np = None  # type: ignore[assignment]
-    OCR_PIPELINE_AVAILABLE = False
-    logger.warning("OCR pipeline imports not available in upload module")
-
-# Sub-module imports (already split out)
-from vocab_worksheet_generation import (
-    convert_pdf_page_to_image,
-    convert_pdf_to_images,
-    get_pdf_page_count,
-)
-from vocab_worksheet_extraction import extract_vocabulary_from_image
-
-try:
-    from vocab_worksheet_ocr import _run_ocr_pipeline_for_page
-except ImportError:
-    _run_ocr_pipeline_for_page = None  # type: ignore[assignment]
-    logger.warning("vocab_worksheet_ocr not available — process-single-page disabled")
-
-# ---------------------------------------------------------------------------
-# In-memory session store (shared with main module)
-# ---------------------------------------------------------------------------
-
-def _get_sessions():
-    from vocab_worksheet_api import _sessions
-    return _sessions
-
-# ---------------------------------------------------------------------------
-# Router (no prefix — will be included into the main vocab router)
-# ---------------------------------------------------------------------------
-
-upload_router = APIRouter()
-
-
-# =============================================================================
-# POST /sessions/{session_id}/upload-pdf-info
-# =============================================================================
-
-@upload_router.post("/sessions/{session_id}/upload-pdf-info")
-async def upload_pdf_get_info(
-    session_id: str,
-    file: UploadFile = File(...),
-):
-    """
-    Upload a PDF and get page count and thumbnails for preview.
-    Use this before processing to let user select pages.
-    """
-    logger.info(f"PDF info request for session {session_id}")
-
-    if session_id not in _get_sessions():
-        raise HTTPException(status_code=404, detail="Session not found")
-
-    session = _get_sessions()[session_id]
-
-    # Validate file type
-    extension = file.filename.split('.')[-1].lower() if file.filename else ''
-    content_type = file.content_type or ''
-
-    if extension != 'pdf' and content_type != 'application/pdf':
-        raise HTTPException(status_code=400, detail="Only PDF files supported for this endpoint")
-
-    content = await file.read()
-
-    # Save PDF temporarily
-    session_dir = os.path.join(LOCAL_STORAGE_PATH, session_id)
-    os.makedirs(session_dir, exist_ok=True)
-    pdf_path = os.path.join(session_dir, "source.pdf")
-
-    with open(pdf_path, 'wb') as f:
-        f.write(content)
-
-    # Get page count
-    page_count = get_pdf_page_count(content)
-
-    # Store PDF data in session for later processing
-    session["pdf_data"] = content
-    session["pdf_path"] = pdf_path
-    session["pdf_page_count"] = page_count
-    session["status"] = "pdf_uploaded"
-
-    # Detect orientation for each page so thumbnails are shown correctly
-    page_rotations: dict = {}
-    if OCR_PIPELINE_AVAILABLE:
-        for pg in range(page_count):
-            try:
-                img_bgr = render_pdf_high_res(content, pg, zoom=2.0)
-                _, rotation = detect_and_fix_orientation(img_bgr)
-                if rotation:
-                    page_rotations[pg] = rotation
-                    logger.info(f"Page {pg + 1}: orientation {rotation}°")
-            except Exception as e:
-                logger.warning(f"Orientation detection failed for page {pg + 1}: {e}")
-    session["page_rotations"] = page_rotations
-
-    return {
-        "session_id": session_id,
-        "page_count": page_count,
-        "filename": file.filename,
-        "page_rotations": page_rotations,
-    }
-
-
-# =============================================================================
-# GET /sessions/{session_id}/pdf-thumbnail/{page_number}
-# =============================================================================
-
-@upload_router.get("/sessions/{session_id}/pdf-thumbnail/{page_number}")
-async def get_pdf_thumbnail(session_id: str, page_number: int, hires: bool = Query(False)):
-    """Get a thumbnail image of a specific PDF page.
-
-    Uses fitz for rendering so that page_rotations (from OCR orientation
-    detection) are applied consistently.
-
-    Args:
-        hires: If True, return full-resolution image (zoom=2.0) instead of thumbnail (zoom=0.5).
-    """
-    if session_id not in _get_sessions():
-        raise HTTPException(status_code=404, detail="Session not found")
-
-    session = _get_sessions()[session_id]
-    pdf_data = session.get("pdf_data")
-
-    if not pdf_data:
-        raise HTTPException(status_code=400, detail="No PDF uploaded for this session")
-
-    try:
-        import fitz
-        zoom = 2.0 if hires else 0.5
-        pdf_document = fitz.open(stream=pdf_data, filetype="pdf")
-        page = pdf_document[page_number]
-        # Apply orientation correction detected during OCR processing
-        rot = session.get("page_rotations", {}).get(page_number, 0)
-        if rot:
-            page.set_rotation(rot)
-        mat = fitz.Matrix(zoom, zoom)
-        pix = page.get_pixmap(matrix=mat)
-        png_data = pix.tobytes("png")
-        pdf_document.close()
-    except Exception as e:
-        logger.error(f"PDF thumbnail failed: {e}")
-        raise HTTPException(status_code=500, detail=f"PDF rendering failed: {str(e)}")
-
-    return StreamingResponse(
-        io.BytesIO(png_data),
-        media_type="image/png",
-    )
-
-
-# =============================================================================
-# GET /sessions/{session_id}/pdf-page-image/{page_number}
-# =============================================================================
-
-@upload_router.get("/sessions/{session_id}/pdf-page-image/{page_number}")
-async def get_pdf_page_image(session_id: str, page_number: int, zoom: float = Query(2.0, ge=0.5, le=4.0)):
-    """PDF page as PNG at arbitrary resolution (for editor view).
-
-    Args:
-        zoom: Zoom factor (0.5=72DPI, 1.0=144DPI, 2.0=288DPI, 4.0=576DPI).
-    """
-    if session_id not in _get_sessions():
-        raise HTTPException(status_code=404, detail="Session not found")
-
-    session = _get_sessions()[session_id]
-    pdf_data = session.get("pdf_data")
-
-    if not pdf_data:
-        raise HTTPException(status_code=400, detail="No PDF uploaded for this session")
-
-    page_count = session.get("pdf_page_count", 1)
-    if page_number < 0 or page_number >= page_count:
-        raise HTTPException(status_code=400, detail=f"Invalid page number. PDF has {page_count} pages (0-indexed).")
-
-    try:
-        import fitz
-        pdf_document = fitz.open(stream=pdf_data, filetype="pdf")
-        page = pdf_document[page_number]
-        # Apply orientation correction detected during OCR processing
-        rot = session.get("page_rotations", {}).get(page_number, 0)
-        if rot:
-            page.set_rotation(rot)
-        mat = fitz.Matrix(zoom, zoom)
-        pix = page.get_pixmap(matrix=mat)
-        png_data = pix.tobytes("png")
-        pdf_document.close()
-        logger.info(f"PDF page {page_number} rendered at zoom={zoom} rot={rot}: {len(png_data)} bytes")
-    except Exception as e:
-        logger.error(f"PDF page image failed: {e}")
-        raise HTTPException(status_code=500, detail=f"PDF rendering failed: {str(e)}")
-
-    return StreamingResponse(
-        io.BytesIO(png_data),
-        media_type="image/png",
-    )
-
-
-# =============================================================================
-# POST /sessions/{session_id}/process-single-page/{page_number}
-# =============================================================================
-
-@upload_router.post("/sessions/{session_id}/process-single-page/{page_number}")
-async def process_single_page(
-    session_id: str,
-    page_number: int,
-    ipa_mode: str = Query("none", pattern="^(auto|all|de|en|none)$"),
-    syllable_mode: str = Query("none", pattern="^(auto|all|de|en|none)$"),
-    enhance: bool = Query(True, description="Step 3: CLAHE + Denoise for degraded scans"),
-    max_cols: int = Query(3, description="Step 2: Max column count (0=unlimited)"),
-    min_conf: int = Query(0, description="Step 1: Min OCR confidence (0=auto from quality score)"),
-):
-    """
-    Process a SINGLE page of an uploaded PDF using the Kombi OCR pipeline.
-
-    Uses the full Kombi pipeline (orientation -> deskew -> dewarp -> crop ->
-    dual-engine OCR -> grid-build with autocorrect/merge) for best quality.
-
-    Query params:
-        ipa_mode: "none" (default), "auto", "all", "en", "de"
-        syllable_mode: "none" (default), "auto", "all", "en", "de"
-        enhance: true (default) -- apply CLAHE/denoise for degraded scans
-        max_cols: 3 (default) -- max column count (0=unlimited)
-        min_conf: 0 (default=auto) -- min OCR confidence (0=from quality score)
-
-    The frontend should call this sequentially for each page.
-    Returns the vocabulary for just this one page.
-    """
-    logger.info(f"Processing SINGLE page {page_number + 1} for session {session_id}")
-
-    if session_id not in _get_sessions():
-        raise HTTPException(
-            status_code=404,
-            detail="Session nicht im Speicher. Bitte erstellen Sie eine neue Session und laden Sie das PDF erneut hoch.",
-        )
-
-    session = _get_sessions()[session_id]
-    pdf_data = session.get("pdf_data")
-
-    if not pdf_data:
-        raise HTTPException(status_code=400, detail="No PDF uploaded for this session")
-
-    page_count = session.get("pdf_page_count", 1)
-
-    if page_number < 0 or page_number >= page_count:
-        raise HTTPException(status_code=400, detail=f"Invalid page number. PDF has {page_count} pages (0-indexed).")
-
-    # Derive pipeline-level variable names for the quality report
-    enable_enhance = enhance
-    max_columns = max_cols if max_cols > 0 else None
-    override_min_conf = min_conf if min_conf > 0 else None
-
-    # --- OCR Pipeline path (use same render_pdf_high_res as admin OCR pipeline) ---
-    rotation_deg = 0
-    quality_report = None
-    min_ocr_conf = 40  # default; overridden by pipeline when quality report is available
-    if OCR_PIPELINE_AVAILABLE and _run_ocr_pipeline_for_page is not None:
-        try:
-            img_bgr = render_pdf_high_res(pdf_data, page_number, zoom=3.0)
-            page_vocabulary, rotation_deg, quality_report = await _run_ocr_pipeline_for_page(
-                img_bgr, page_number, session_id,
-                ipa_mode=ipa_mode, syllable_mode=syllable_mode,
-                enable_enhance=enable_enhance,
-                max_columns=max_columns,
-                override_min_conf=override_min_conf,
-            )
-            # Update min_ocr_conf from quality report if available
-            if quality_report and hasattr(quality_report, 'recommended_min_conf'):
-                min_ocr_conf = quality_report.recommended_min_conf
-        except Exception as e:
-            logger.error(f"OCR pipeline failed for page {page_number + 1}: {e}", exc_info=True)
-            return {
-                "session_id": session_id,
-                "page_number": page_number + 1,
-                "success": False,
-                "error": f"OCR pipeline error: {e}",
-                "vocabulary": [],
-                "vocabulary_count": 0,
-            }
-    else:
-        # Fallback to LLM vision extraction
-        logger.warning("OCR pipeline not available, falling back to LLM vision")
-        image_data = await convert_pdf_page_to_image(pdf_data, page_number, thumbnail=False)
-        vocabulary, confidence, error = await extract_vocabulary_from_image(
-            image_data,
-            f"page_{page_number + 1}.png",
-            page_number=page_number
-        )
-        if error:
-            logger.warning(f"Page {page_number + 1} failed: {error}")
-            return {
-                "session_id": session_id,
-                "page_number": page_number + 1,
-                "success": False,
-                "error": error,
-                "vocabulary": [],
-                "vocabulary_count": 0,
-            }
-        page_vocabulary = []
-        for entry in vocabulary:
-            entry_dict = entry.dict() if hasattr(entry, 'dict') else (entry.__dict__.copy() if hasattr(entry, '__dict__') else dict(entry))
-            entry_dict['source_page'] = page_number + 1
-            if 'id' not in entry_dict or not entry_dict['id']:
-                entry_dict['id'] = str(uuid.uuid4())
-            page_vocabulary.append(entry_dict)
-
-    logger.info(f"Page {page_number + 1}: {len(page_vocabulary)} Vokabeln extrahiert")
-
-    # Store rotation for this page (used by image/thumbnail endpoints)
-    session.setdefault("page_rotations", {})[page_number] = rotation_deg
-
-    # Add to session's vocabulary (append, don't replace)
-    existing_vocab = session.get("vocabulary", [])
-    # Remove any existing entries from this page (in case of re-processing)
-    existing_vocab = [v for v in existing_vocab if v.get("source_page") != page_number + 1]
-    existing_vocab.extend(page_vocabulary)
-    session["vocabulary"] = existing_vocab
-    session["vocabulary_count"] = len(existing_vocab)
-    session["status"] = SessionStatus.EXTRACTED.value
-
-    result = {
-        "session_id": session_id,
-        "page_number": page_number + 1,
-        "success": True,
-        "vocabulary": page_vocabulary,
-        "vocabulary_count": len(page_vocabulary),
-        "total_vocabulary_count": len(existing_vocab),
-        "extraction_confidence": 0.9,
-        "rotation": rotation_deg,
-    }
-
-    # Add scan quality report + active steps info
-    if quality_report:
-        sq = quality_report.to_dict()
-        sq["active_steps"] = {
-            "step1_confidence": f"min_conf={min_ocr_conf}" if not override_min_conf else f"min_conf={override_min_conf} (override)",
-            "step2_max_columns": f"max_cols={max_columns}" if max_columns else "unlimited",
-            "step3_enhance": "on" if enable_enhance and quality_report.is_degraded else "off",
-        }
-        result["scan_quality"] = sq
-
-    return result
-
-
-# =============================================================================
-# POST /sessions/{session_id}/process-pages  (DEPRECATED)
-# =============================================================================
-
-@upload_router.post("/sessions/{session_id}/process-pages")
-async def process_pdf_pages(
-    session_id: str,
-    pages: List[int] = None,
-    process_all: bool = False,
-):
-    """
-    Process specific pages of an uploaded PDF.
-
-    DEPRECATED: Use /process-single-page/{page_number} instead for better results.
-
-    Args:
-        pages: List of 0-indexed page numbers to process
-        process_all: If True, process all pages
-    """
-    logger.info(f"Process pages request for session {session_id}: pages={pages}, process_all={process_all}")
-
-    if session_id not in _get_sessions():
-        raise HTTPException(status_code=404, detail="Session not found")
-
-    session = _get_sessions()[session_id]
-    pdf_data = session.get("pdf_data")
-
-    if not pdf_data:
-        raise HTTPException(status_code=400, detail="No PDF uploaded for this session")
-
-    page_count = session.get("pdf_page_count", 1)
-
-    # Determine which pages to process
-    if process_all:
-        pages = list(range(page_count))
-    elif pages is None or len(pages) == 0:
-        pages = [0]  # Default to first page
-
-    # Convert selected pages to images
-    images = await convert_pdf_to_images(pdf_data, pages)
-
-    # Extract vocabulary from each page SEQUENTIALLY
-    all_vocabulary = []
-    total_confidence = 0.0
-    successful_pages = []
-    failed_pages = []
-    error_messages = []
-
-    for i, image_data in enumerate(images):
-        page_num = pages[i]
-        logger.info(f"Extracting vocabulary from page {page_num + 1} of {len(images)}...")
-
-        vocabulary, confidence, error = await extract_vocabulary_from_image(
-            image_data,
-            f"page_{page_num + 1}.png",
-            page_number=page_num
-        )
-
-        if error:
-            failed_pages.append(page_num + 1)
-            error_messages.append(error)
-            logger.warning(f"Page {page_num + 1} failed: {error}")
-        else:
-            successful_pages.append(page_num + 1)
-            total_confidence += confidence
-
-            # Add page info to each entry and convert to dict
-            for entry in vocabulary:
-                entry_dict = entry.dict() if hasattr(entry, 'dict') else (entry.__dict__.copy() if hasattr(entry, '__dict__') else dict(entry))
-                entry_dict['source_page'] = page_num + 1
-                all_vocabulary.append(entry_dict)
-
-            logger.info(f"Page {page_num + 1}: {len(vocabulary)} Vokabeln extrahiert")
-
-    avg_confidence = total_confidence / len(successful_pages) if successful_pages else 0
-
-    # Update session
-    session["vocabulary"] = all_vocabulary
-    session["vocabulary_count"] = len(all_vocabulary)
-    session["extraction_confidence"] = avg_confidence
-    session["processed_pages"] = pages
-    session["successful_pages"] = successful_pages
-    session["failed_pages"] = failed_pages
-    session["status"] = SessionStatus.EXTRACTED.value
-
-    # Save first page as preview image
-    if images:
-        session_dir = os.path.join(LOCAL_STORAGE_PATH, session_id)
-        image_path = os.path.join(session_dir, "source.png")
-        with open(image_path, 'wb') as f:
-            f.write(images[0])
-        session["image_path"] = image_path
-
-    result = {
-        "session_id": session_id,
-        "pages_processed": len(pages),
-        "pages_successful": len(successful_pages),
-        "pages_failed": len(failed_pages),
-        "successful_pages": successful_pages,
-        "failed_pages": failed_pages,
-        "vocabulary_count": len(all_vocabulary),
-        "extraction_confidence": avg_confidence,
-        "status": SessionStatus.EXTRACTED.value,
-    }
-
-    if error_messages:
-        result["errors"] = error_messages
-
-    return result