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Step 5i was overwriting IPA-corrected text from Step 5c when reconstructing cells from word_boxes. Added _ipa_corrected flag to preserve corrections. Also tightened merged-token prefix matching (min prefix 4 chars, min suffix 3 chars) to prevent false positives like "sis" being extracted from "si:said". Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
1644 lines
73 KiB
Python
1644 lines
73 KiB
Python
"""
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Grid Editor API — builds a structured, zone-aware grid from Kombi OCR results.
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Takes the merged word positions from paddle-kombi / rapid-kombi and:
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1. Detects bordered boxes on the image (cv_box_detect)
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2. Splits the page into zones (content + box regions)
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3. Clusters words into columns and rows per zone
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4. Returns a hierarchical StructuredGrid for the frontend Excel-like editor
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Lizenz: Apache 2.0 (kommerziell nutzbar)
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DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
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"""
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import logging
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import re
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import time
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from typing import Any, Dict, List, Optional, Tuple
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import cv2
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import numpy as np
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from fastapi import APIRouter, HTTPException, Request
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from cv_box_detect import detect_boxes, split_page_into_zones
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from cv_graphic_detect import detect_graphic_elements
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from cv_vocab_types import PageZone
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from cv_color_detect import detect_word_colors, recover_colored_text
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from cv_ocr_engines import fix_cell_phonetics, fix_ipa_continuation_cell, _text_has_garbled_ipa, _lookup_ipa, _words_to_reading_order_text, _group_words_into_lines
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from ocr_pipeline_session_store import (
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get_session_db,
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get_session_image,
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update_session_db,
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)
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from grid_editor_helpers import (
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_filter_border_strip_words,
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_cluster_columns_by_alignment,
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_GRID_GHOST_CHARS,
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_filter_border_ghosts,
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_MARKER_CHARS,
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_merge_inline_marker_columns,
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_flatten_word_boxes,
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_words_in_zone,
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_PIPE_RE_VSPLIT,
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_detect_vertical_dividers,
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_split_zone_at_vertical_dividers,
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_merge_content_zones_across_boxes,
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_detect_heading_rows_by_color,
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_detect_heading_rows_by_single_cell,
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_detect_header_rows,
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_build_zone_grid,
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_get_content_bounds,
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_filter_decorative_margin,
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_filter_footer_words,
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_filter_header_junk,
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)
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/v1/ocr-pipeline", tags=["grid-editor"])
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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# ---------------------------------------------------------------------------
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# Core computation (used by build-grid endpoint and regression tests)
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# ---------------------------------------------------------------------------
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async def _build_grid_core(session_id: str, session: dict) -> dict:
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"""Core grid building logic — pure computation, no HTTP or DB side effects.
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Args:
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session_id: Session identifier (for logging and image loading).
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session: Full session dict from get_session_db().
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Returns:
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StructuredGrid result dict.
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Raises:
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ValueError: If session data is incomplete.
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"""
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t0 = time.time()
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# 1. Validate and load word results
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word_result = session.get("word_result")
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if not word_result or not word_result.get("cells"):
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raise ValueError("No word results found. Run paddle-kombi or rapid-kombi first.")
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img_w = word_result.get("image_width", 0)
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img_h = word_result.get("image_height", 0)
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if not img_w or not img_h:
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raise ValueError("Missing image dimensions in word_result")
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# 2. Flatten all word boxes from cells
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all_words = _flatten_word_boxes(word_result["cells"])
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if not all_words:
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raise ValueError("No word boxes found in cells")
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logger.info("build-grid session %s: %d words from %d cells",
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session_id, len(all_words), len(word_result["cells"]))
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# 2b. Filter decorative margin columns (alphabet graphics).
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# Some worksheets have a decorative alphabet strip along one margin
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# (A-Z in a graphic). OCR reads these as single-char words aligned
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# vertically. Detect and remove them before grid building.
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margin_strip_info = _filter_decorative_margin(all_words, img_w, logger, session_id)
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margin_strip_detected = margin_strip_info.get("found", False)
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# Read document_category from session (user-selected or auto-detected)
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document_category = session.get("document_category")
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# 2c. Filter footer rows (page numbers at the very bottom).
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# Isolated short text in the bottom 5% of the page is typically a
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# page number ("64", "S. 12") and not real content.
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_filter_footer_words(all_words, img_h, logger, session_id)
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# 2c2. Filter OCR junk from header illustrations.
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# Low-confidence short fragments above the first real content row.
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_filter_header_junk(all_words, img_h, logger, session_id)
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# 2d. Filter words inside user-defined exclude regions (from Structure step).
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# These are explicitly marked by the user, so ALL words inside are removed
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# regardless of confidence.
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structure_result = session.get("structure_result")
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exclude_rects = []
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if structure_result:
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for er in structure_result.get("exclude_regions", []):
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exclude_rects.append({
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"x": er["x"], "y": er["y"],
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"w": er["w"], "h": er["h"],
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})
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if exclude_rects:
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before = len(all_words)
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filtered = []
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for w in all_words:
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w_cx = w["left"] + w.get("width", 0) / 2
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w_cy = w["top"] + w.get("height", 0) / 2
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inside = any(
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er["x"] <= w_cx <= er["x"] + er["w"]
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and er["y"] <= w_cy <= er["y"] + er["h"]
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for er in exclude_rects
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)
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if not inside:
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filtered.append(w)
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removed = before - len(filtered)
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if removed:
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all_words = filtered
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logger.info(
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"build-grid session %s: removed %d words inside %d user exclude region(s)",
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session_id, removed, len(exclude_rects),
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)
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# 2e. Hard-filter words inside graphic/image regions from structure step.
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# ALL words inside graphic regions are removed regardless of confidence —
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# images cannot contain real text; any OCR words inside are artifacts.
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# After image loading (Step 3a) we augment these with freshly detected
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# graphic regions from cv_graphic_detect.
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graphic_rects: List[Dict[str, int]] = []
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if structure_result:
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for g in structure_result.get("graphics", []):
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graphic_rects.append({
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"x": g["x"], "y": g["y"],
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"w": g["w"], "h": g["h"],
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})
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if graphic_rects:
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before = len(all_words)
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all_words = [
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w for w in all_words
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if not any(
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gr["x"] <= w["left"] + w.get("width", 0) / 2 <= gr["x"] + gr["w"]
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and gr["y"] <= w["top"] + w.get("height", 0) / 2 <= gr["y"] + gr["h"]
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for gr in graphic_rects
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)
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]
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removed = before - len(all_words)
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if removed:
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logger.info(
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"build-grid session %s: hard-removed %d words inside %d structure graphic region(s)",
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session_id, removed, len(graphic_rects),
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)
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# 3. Load image for box detection
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img_png = await get_session_image(session_id, "cropped")
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if not img_png:
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img_png = await get_session_image(session_id, "dewarped")
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if not img_png:
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img_png = await get_session_image(session_id, "original")
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zones_data: List[Dict[str, Any]] = []
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boxes_detected = 0
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recovered_count = 0
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border_prefiltered = False
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img_bgr = None
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content_x, content_y, content_w, content_h = _get_content_bounds(all_words)
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if img_png:
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# Decode image for color detection + box detection
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arr = np.frombuffer(img_png, dtype=np.uint8)
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img_bgr = cv2.imdecode(arr, cv2.IMREAD_COLOR)
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if img_bgr is not None:
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# --- 3a. Detect graphic/image regions via CV and hard-filter ---
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# Pass only significant words (len >= 3) to the detector so that
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# short OCR artifacts inside images don't fool the text-vs-graphic
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# heuristic (it counts word centroids to distinguish text from images).
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sig_words = [w for w in all_words if len((w.get("text") or "").strip()) >= 3]
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fresh_graphics = detect_graphic_elements(img_bgr, sig_words)
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if fresh_graphics:
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fresh_rects = [
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{"x": g.x, "y": g.y, "w": g.width, "h": g.height}
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for g in fresh_graphics
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]
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graphic_rects.extend(fresh_rects)
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logger.info(
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"build-grid session %s: detected %d graphic region(s) via CV",
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session_id, len(fresh_graphics),
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)
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# Hard-filter words inside newly detected graphic regions
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before = len(all_words)
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all_words = [
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w for w in all_words
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if not any(
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gr["x"] <= w["left"] + w.get("width", 0) / 2 <= gr["x"] + gr["w"]
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and gr["y"] <= w["top"] + w.get("height", 0) / 2 <= gr["y"] + gr["h"]
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for gr in fresh_rects
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)
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]
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removed = before - len(all_words)
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if removed:
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logger.info(
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"build-grid session %s: hard-removed %d words inside %d fresh graphic region(s)",
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session_id, removed, len(fresh_rects),
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)
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# --- Recover colored text that OCR missed (before grid building) ---
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recovered = recover_colored_text(img_bgr, all_words)
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if recovered and graphic_rects:
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# Filter recovered chars inside graphic regions
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recovered = [
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r for r in recovered
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if not any(
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gr["x"] <= r["left"] + r.get("width", 0) / 2 <= gr["x"] + gr["w"]
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and gr["y"] <= r["top"] + r.get("height", 0) / 2 <= gr["y"] + gr["h"]
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for gr in graphic_rects
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)
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]
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if recovered:
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recovered_count = len(recovered)
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all_words.extend(recovered)
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logger.info(
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"build-grid session %s: +%d recovered colored words",
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session_id, recovered_count,
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)
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# Detect bordered boxes
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boxes = detect_boxes(
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img_bgr,
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content_x=content_x,
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content_w=content_w,
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content_y=content_y,
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content_h=content_h,
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)
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boxes_detected = len(boxes)
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if boxes:
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# Filter border ghost words before grid building
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all_words, ghost_count = _filter_border_ghosts(all_words, boxes)
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if ghost_count:
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logger.info(
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"build-grid session %s: removed %d border ghost words",
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session_id, ghost_count,
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)
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# Split page into zones
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page_zones = split_page_into_zones(
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content_x, content_y, content_w, content_h, boxes
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)
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# Merge content zones separated by box zones
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page_zones = _merge_content_zones_across_boxes(
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page_zones, content_x, content_w
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)
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# 3b. Detect vertical dividers and split content zones
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vsplit_group_counter = 0
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expanded_zones: List = []
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for pz in page_zones:
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if pz.zone_type != "content":
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expanded_zones.append(pz)
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continue
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zone_words = _words_in_zone(
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all_words, pz.y, pz.height, pz.x, pz.width
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)
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divider_xs = _detect_vertical_dividers(
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zone_words, pz.x, pz.width, pz.y, pz.height
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)
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if divider_xs:
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sub_zones = _split_zone_at_vertical_dividers(
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pz, divider_xs, vsplit_group_counter
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)
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expanded_zones.extend(sub_zones)
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vsplit_group_counter += 1
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# Remove pipe words so they don't appear in sub-zones
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pipe_ids = set(
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id(w) for w in zone_words
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if _PIPE_RE_VSPLIT.match((w.get("text") or "").strip())
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)
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all_words[:] = [w for w in all_words if id(w) not in pipe_ids]
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logger.info(
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"build-grid: vertical split zone %d at x=%s → %d sub-zones",
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pz.index, [int(x) for x in divider_xs], len(sub_zones),
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)
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else:
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expanded_zones.append(pz)
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# Re-index zones
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for i, pz in enumerate(expanded_zones):
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pz.index = i
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page_zones = expanded_zones
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||
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# --- Union columns from all content zones ---
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# Each content zone detects columns independently. Narrow
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# columns (page refs, markers) may appear in only one zone.
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# Merge column split-points from ALL content zones so every
|
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# zone shares the full column set.
|
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# NOTE: Zones from a vertical split are independent and must
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# NOT share columns with each other.
|
||
|
||
# First pass: build grids per zone independently
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zone_grids: List[Dict] = []
|
||
|
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for pz in page_zones:
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zone_words = _words_in_zone(
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all_words, pz.y, pz.height, pz.x, pz.width
|
||
)
|
||
# Filter recovered single-char artifacts in ALL zones
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||
# (decorative colored pixel blobs like !, ?, • from
|
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# recover_colored_text that don't represent real text)
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||
before = len(zone_words)
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||
zone_words = [
|
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w for w in zone_words
|
||
if not (
|
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w.get("recovered")
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and len(w.get("text", "").strip()) <= 2
|
||
)
|
||
]
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||
removed = before - len(zone_words)
|
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if removed:
|
||
logger.info(
|
||
"build-grid: filtered %d recovered artifacts from %s zone %d",
|
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removed, pz.zone_type, pz.index,
|
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)
|
||
# Filter words inside image overlay regions (merged box zones)
|
||
if pz.image_overlays:
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||
before_ov = len(zone_words)
|
||
zone_words = [
|
||
w for w in zone_words
|
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if not any(
|
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ov["y"] <= w["top"] + w["height"] / 2 <= ov["y"] + ov["height"]
|
||
and ov["x"] <= w["left"] + w["width"] / 2 <= ov["x"] + ov["width"]
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for ov in pz.image_overlays
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||
)
|
||
]
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ov_removed = before_ov - len(zone_words)
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||
if ov_removed:
|
||
logger.info(
|
||
"build-grid: filtered %d words inside image overlays from zone %d",
|
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ov_removed, pz.index,
|
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)
|
||
zone_words, bs_removed = _filter_border_strip_words(zone_words)
|
||
if bs_removed:
|
||
border_prefiltered = True
|
||
logger.info(
|
||
"build-grid: pre-filtered %d border-strip words from zone %d",
|
||
bs_removed, pz.index,
|
||
)
|
||
grid = _build_zone_grid(
|
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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})
|
||
|
||
# Second pass: merge column boundaries from all content zones
|
||
# Exclude zones from vertical splits — they have independent columns.
|
||
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:
|
||
# 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 all_split_xs:
|
||
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
|
||
)
|
||
|
||
# Apply union whenever it has at least as many
|
||
# columns as the best single zone. Even with the
|
||
# same count the union boundaries are better because
|
||
# they incorporate evidence from all zones.
|
||
if total_cols >= max_zone_cols:
|
||
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,
|
||
)
|
||
|
||
for zg in zone_grids:
|
||
pz = zg["pz"]
|
||
grid = zg["grid"]
|
||
# Remove internal _raw_columns before adding to response
|
||
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:
|
||
# Filter recovered single-char artifacts (same as in zone loop above)
|
||
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,
|
||
)
|
||
# Pre-filter border-strip words so column detection is not
|
||
# confused by edge artifacts. When this removes words, Step 4e
|
||
# is skipped (it would otherwise re-detect content as a "strip").
|
||
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,
|
||
})
|
||
|
||
# 4b. Remove junk rows: rows where ALL cells contain only short,
|
||
# low-confidence text (OCR noise, stray marks). Real vocabulary rows
|
||
# have at least one word with conf >= 50 or meaningful text length.
|
||
# Also remove "oversized stub" rows: rows with ≤2 very short words
|
||
# whose word-boxes are significantly taller than the median (e.g.
|
||
# large red page numbers like "( 9" that are not real text content).
|
||
_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 > 1.8× median (page numbers, stray marks,
|
||
# OCR from illustration labels like "SEA &")
|
||
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)
|
||
if len(total_text) <= 5 and max_h > median_wb_h * 1.8:
|
||
junk_row_indices.add(ri)
|
||
continue
|
||
|
||
# Rule 3: scattered debris — rows with only tiny fragments
|
||
# (e.g. OCR artifacts from illustrations/graphics).
|
||
# If the row has no word longer than 2 chars, it's noise.
|
||
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),
|
||
)
|
||
|
||
# 4c. Remove oversized word_boxes from individual cells.
|
||
# OCR artifacts from graphics/images (e.g. a huge "N" from a map image)
|
||
# have word heights 3-5x the median. Remove them per-word so they don't
|
||
# pollute cells that also contain valid text in other columns.
|
||
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:
|
||
# Remove cells that became empty after oversized removal
|
||
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),
|
||
)
|
||
|
||
# 4d. Remove pipe-character word_boxes (column divider artifacts).
|
||
# OCR reads physical vertical divider lines as "|" or "||" characters.
|
||
# These sit at consistent x positions near column boundaries and pollute
|
||
# cell text. Remove them from word_boxes and rebuild cell text.
|
||
# NOTE: Zones from a vertical split already had pipes removed in step 3b.
|
||
_PIPE_RE = re.compile(r"^\|+$")
|
||
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)
|
||
# Remove cells that became empty after pipe removal
|
||
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 word_boxes/cells where the pipe is an
|
||
# OCR column-divider artifact. Preserve pipes that are embedded in
|
||
# words as syllable separators (e.g. "zu|trau|en") — these are
|
||
# intentional and used in dictionary Ground Truth.
|
||
for z in zones_data:
|
||
for cell in z.get("cells", []):
|
||
for wb in cell.get("word_boxes", []):
|
||
wbt = wb.get("text", "")
|
||
# Only strip if the ENTIRE word_box is just pipe(s)
|
||
# (handled by _PIPE_RE above) — leave embedded pipes alone
|
||
text = cell.get("text", "")
|
||
if "|" in text:
|
||
# Only strip leading/trailing pipes (OCR artifacts at cell edges)
|
||
cleaned = text.strip("|").strip()
|
||
if cleaned != text.strip():
|
||
cell["text"] = cleaned
|
||
|
||
# 4e. Detect and remove page-border decoration strips.
|
||
# Skipped when the pre-filter already removed border words BEFORE
|
||
# column detection — re-running would incorrectly detect the
|
||
# leftmost content column as a "strip".
|
||
border_strip_removed = 0
|
||
if border_prefiltered:
|
||
logger.info("Step 4e: skipped (border pre-filter already applied)")
|
||
else:
|
||
# Some textbooks have decorative alphabet strips along the page
|
||
# edge. OCR picks up scattered letters from these as artifacts.
|
||
# Detection: find the first significant x-gap (>30 px) from each
|
||
# page edge between a small cluster (<20 %) and the main content.
|
||
for z in zones_data:
|
||
cells = z.get("cells", [])
|
||
if not cells:
|
||
continue
|
||
all_wbs_with_cell: List[tuple] = [] # (left, wb, cell)
|
||
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,
|
||
)
|
||
|
||
# 4f. Remove decorative edge columns (alphabet sidebar safety net).
|
||
# Dictionary pages have A-Z letter sidebars that OCR reads as single-
|
||
# character word_boxes. These form narrow columns with very short text.
|
||
# Detection: edge column where almost ALL cells are single characters.
|
||
for z in zones_data:
|
||
columns = z.get("columns", [])
|
||
cells = z.get("cells", [])
|
||
if len(columns) < 3 or not cells:
|
||
continue
|
||
# Group cells by col_type (skip spanning_header)
|
||
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
|
||
# Key criterion: average text length and single-char ratio.
|
||
# Alphabet sidebars have avg_len ≈ 1.0 and nearly all cells
|
||
# are single characters.
|
||
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 # real content has longer text
|
||
if single_ratio < 0.7:
|
||
continue # not dominated by single chars
|
||
# Remove this edge column
|
||
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
|
||
|
||
# 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 (after color is available)
|
||
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
|
||
# OCR often misses opening "(" while detecting closing ")".
|
||
# If a cell's text has ")" without a matching "(", prepend "(".
|
||
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 — replace garbled OCR phonetics with
|
||
# correct IPA from the dictionary (same as in the OCR pipeline).
|
||
# Only applies to vocabulary tables (≥3 columns: EN | article | DE).
|
||
# Single/two-column layouts are continuous text, not vocab tables.
|
||
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
|
||
if total_cols >= 3:
|
||
# Find the column that contains IPA brackets → English headwords.
|
||
# Count cells with bracket patterns per col_type. The column with
|
||
# the most brackets is the headword column (IPA sits after or below
|
||
# headwords). Falls back to longest-average if no brackets found.
|
||
col_bracket_count: Dict[str, int] = {}
|
||
col_avg_len: Dict[str, List[int]] = {}
|
||
for cell in all_cells:
|
||
ct = cell.get("col_type", "")
|
||
txt = cell.get("text", "") or ""
|
||
col_avg_len.setdefault(ct, []).append(len(txt))
|
||
if ct.startswith("column_") and '[' in txt:
|
||
col_bracket_count[ct] = col_bracket_count.get(ct, 0) + 1
|
||
# Pick column with most bracket IPA patterns
|
||
if col_bracket_count:
|
||
en_col_type = max(col_bracket_count, key=col_bracket_count.get)
|
||
else:
|
||
# Fallback: longest average text
|
||
best_avg = 0
|
||
for ct, lengths in col_avg_len.items():
|
||
if not ct.startswith("column_"):
|
||
continue
|
||
avg = sum(lengths) / len(lengths) if lengths else 0
|
||
if avg > best_avg:
|
||
best_avg = avg
|
||
en_col_type = ct
|
||
if en_col_type:
|
||
for cell in all_cells:
|
||
if cell.get("col_type") == en_col_type:
|
||
cell["_orig_col_type"] = en_col_type
|
||
cell["col_type"] = "column_en"
|
||
# Snapshot text before IPA fix to detect which cells were modified
|
||
_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
|
||
# Mark cells whose text was changed by IPA correction so that
|
||
# later steps (5i) don't overwrite the corrected text when
|
||
# reconstructing from word_boxes.
|
||
if cell.get("text", "") != _pre_ipa.get(id(cell), ""):
|
||
cell["_ipa_corrected"] = True
|
||
|
||
# 5d. Fix IPA continuation cells — cells where the printed
|
||
# phonetic transcription wraps to a line below the headword.
|
||
# These contain garbled IPA (e.g. "[n, nn]", "[1uedtX,1]").
|
||
# Replace garbled text with proper IPA looked up from the
|
||
# headword in the previous row's same column.
|
||
# Note: We check ALL columns, not just en_col_type, because
|
||
# the EN headword column may not be the longest-average column.
|
||
_REAL_IPA_CHARS = set("ˈˌəɪɛɒʊʌæɑɔʃʒθðŋ")
|
||
ipa_cont_fixed = 0
|
||
for z in 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:
|
||
# Step 5c may have emptied garbled IPA cells like
|
||
# "[n, nn]" — recover text from word_boxes.
|
||
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:
|
||
# Bracketed continuation: "[n, nn]", "[klaoz 'daun]"
|
||
# Text like "employee [im'ploi:]" is NOT fully
|
||
# bracketed and won't match here.
|
||
if not _text_has_garbled_ipa(cell_text):
|
||
continue
|
||
# Already has proper IPA brackets → skip
|
||
if re.search(r'\[[^\]]*[ˈˌəɪɛɒʊʌæɑɔʃʒθðŋ][^\]]*\]', cell_text):
|
||
continue
|
||
else:
|
||
# Unbracketed continuation: "ska:f – ska:vz",
|
||
# "'sekandarr sku:l". Only treat as IPA
|
||
# continuation if this is the ONLY content cell
|
||
# in the row (single-cell row) and the text is
|
||
# garbled IPA without real IPA Unicode symbols.
|
||
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
|
||
# Has real IPA symbols → already fixed or valid
|
||
if any(c in _REAL_IPA_CHARS for c in cell_text):
|
||
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)
|
||
|
||
# 5e. Heading detection by single-cell rows — black headings like
|
||
# "Theme" that have normal color and height but are the ONLY cell
|
||
# in their row (excluding page_ref column_1). Must run AFTER 5d
|
||
# so IPA continuation cells are already processed.
|
||
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 — headings detected in 5e ran AFTER
|
||
# IPA correction (5c), so they may have dictionary IPA appended
|
||
# (e.g. "Theme [θˈiːm]" → "Theme"). Headings should show the
|
||
# original text only.
|
||
for z in zones_data:
|
||
for cell in z.get("cells", []):
|
||
if cell.get("col_type") != "heading":
|
||
continue
|
||
text = cell.get("text", "")
|
||
# Strip trailing IPA bracket: "Theme [θˈiːm]" → "Theme"
|
||
stripped = re.sub(r'\s*\[[^\]]*\]\s*$', '', text).strip()
|
||
if stripped and stripped != text:
|
||
cell["text"] = stripped
|
||
|
||
# 5g. Extract page_ref cells and footer rows from content zones.
|
||
# Page references (column_1 cells like "p.70") sit in rows that
|
||
# also contain vocabulary — extract them as zone metadata without
|
||
# removing the row. Footer lines (e.g. "two hundred and twelve"
|
||
# = page number at bottom) are standalone rows that should be
|
||
# removed from the table entirely.
|
||
_REAL_IPA_CHARS_SET = set("ˈˌəɪɛɒʊʌæɑɔʃʒθðŋ")
|
||
# Page-ref pattern: "p.70", "P.70", ",.65" (garbled "p"), or bare "70"
|
||
_PAGE_REF_RE = re.compile(r'^[pP,]?\s*\.?\s*\d+$')
|
||
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"))
|
||
|
||
# Remove page_ref cells from the table (but keep their rows)
|
||
if page_ref_cell_ids:
|
||
z["cells"] = [c for c in z["cells"]
|
||
if c.get("cell_id") not in page_ref_cell_ids]
|
||
|
||
# Detect footer: last non-header row if it has only 1 cell
|
||
# and the text is NOT IPA (no real IPA Unicode symbols).
|
||
# This catches page numbers like "two hundred and twelve".
|
||
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()
|
||
# Not IPA (no real IPA symbols) and not a heading
|
||
has_real_ipa = any(c in _REAL_IPA_CHARS_SET for c in text)
|
||
if text and not has_real_ipa 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", {}),
|
||
})
|
||
|
||
# Mark footer rows (keep in table, just tag for frontend)
|
||
if footer_rows:
|
||
footer_ris = {fr["row_index"] for fr in footer_rows}
|
||
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 from zone %d",
|
||
len(page_refs), len(footer_rows), z.get("zone_index", 0),
|
||
)
|
||
|
||
# Store as zone-level metadata
|
||
if page_refs:
|
||
z["page_refs"] = page_refs
|
||
if footer_rows:
|
||
z["footer"] = footer_rows
|
||
|
||
# 5h. Convert slash-delimited IPA to bracket notation.
|
||
# Dictionary-style pages print IPA between slashes: "tiger /'taiga/"
|
||
# Detect the pattern <headword> /ocr_ipa/ and replace with [dict_ipa]
|
||
# using the IPA dictionary when available, falling back to the OCR text.
|
||
# The regex requires a word character (or ² ³) right before the opening
|
||
# slash to avoid false positives like "sb/sth".
|
||
_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 at start of text (headword on previous line)
|
||
_STANDALONE_SLASH_IPA_RE = re.compile(r'^/([^/]{2,})/')
|
||
# IPA between slashes never contains spaces, parentheses, or commas.
|
||
# Reject matches that look like grammar: "sb/sth up a) jdn/"
|
||
_SLASH_IPA_REJECT_RE = re.compile(r'[\s(),]')
|
||
slash_ipa_fixed = 0
|
||
for z in zones_data:
|
||
for cell in z.get("cells", []):
|
||
# Only process English headword column — avoid converting
|
||
# German text like "der/die/das" to IPA.
|
||
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) # includes slashes
|
||
inner_raw = ocr_ipa.strip("/").strip()
|
||
# Reject if inner content has spaces/parens/commas (grammar)
|
||
if _SLASH_IPA_REJECT_RE.search(inner_raw):
|
||
return m.group(0)
|
||
# Strip superscript digits for lookup
|
||
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}]"
|
||
# Fallback: keep OCR IPA but convert slashes to brackets
|
||
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)
|
||
|
||
# Second pass: convert remaining /ipa/ after [ipa] from first pass.
|
||
# Pattern: [ipa] /ipa2/ → [ipa] [ipa2] (second pronunciation variant)
|
||
_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)
|
||
|
||
# Handle standalone /ipa/ at start (no headword in this cell)
|
||
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)
|
||
|
||
# 5i. Remove blue bullet/artifact word_boxes.
|
||
# Dictionary pages have small blue square bullets (■) before entries.
|
||
# OCR reads these as text artifacts (©, e, *, or even plausible words
|
||
# like "fighily" overlapping the real word "tightly").
|
||
# Detection rules:
|
||
# a) Tiny coloured symbols: area < 200 AND conf < 85 (any non-black)
|
||
# b) Overlapping word_boxes: >40% x-overlap → remove lower confidence
|
||
# c) Duplicate text: consecutive blue wbs with identical text, gap < 6px
|
||
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 (bullets, graphic fragments)
|
||
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 (b) + (c): overlap and duplicate detection
|
||
# Sort by x for pairwise comparison
|
||
_ALPHA_WORD_RE = re.compile(r'^[A-Za-z\u00c0-\u024f\-]+[.,;:!?]*$')
|
||
to_merge: List[Tuple[int, int]] = [] # pairs (i1, i2) to merge
|
||
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
|
||
|
||
# (b) Significant x-overlap
|
||
if overlap_pct > 0.20:
|
||
t1 = (w1.get("text") or "").strip()
|
||
t2 = (w2.get("text") or "").strip()
|
||
|
||
# Syllable-split words: both are alphabetic text with
|
||
# moderate overlap (20-75%). Merge instead of removing.
|
||
# OCR splits words at syllable marks, producing overlapping
|
||
# boxes like "zu" + "tiefst" → "zutiefst".
|
||
if (overlap_pct <= 0.75
|
||
and _ALPHA_WORD_RE.match(t1)
|
||
and _ALPHA_WORD_RE.match(t2)):
|
||
to_merge.append((i1, i2))
|
||
continue
|
||
|
||
if overlap_pct <= 0.40:
|
||
continue # too little overlap and not alphabetic merge
|
||
|
||
c1 = w1.get("conf", 50)
|
||
c2 = w2.get("conf", 50)
|
||
|
||
# For very high overlap (>90%) with different text,
|
||
# prefer the word that exists in the IPA dictionary
|
||
# over confidence (OCR can give artifacts high conf).
|
||
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:
|
||
# Same confidence: remove the taller one (bullet slivers)
|
||
if w1.get("height", 0) > w2.get("height", 0):
|
||
to_remove.add(i1)
|
||
else:
|
||
to_remove.add(i2)
|
||
|
||
# (c) Duplicate text: consecutive blue with same text, gap < 6px
|
||
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()):
|
||
# Remove the one with lower confidence; if equal, first one
|
||
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:
|
||
merged_indices: set = set()
|
||
for mi1, mi2 in to_merge:
|
||
if mi1 in to_remove or mi2 in to_remove:
|
||
continue # don't merge if one is being removed
|
||
if mi1 in merged_indices or mi2 in merged_indices:
|
||
continue # already merged
|
||
mw1, mw2 = wbs[mi1], wbs[mi2]
|
||
# Concatenate text (no space — they're parts of one word)
|
||
mt1 = (mw1.get("text") or "").rstrip(".,;:!?")
|
||
mt2 = (mw2.get("text") or "").strip()
|
||
merged_text = mt1 + mt2
|
||
# Union bounding box
|
||
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) # remove the second one
|
||
merged_indices.add(mi1)
|
||
merged_indices.add(mi2)
|
||
bullet_removed -= 1 # net: merge, not removal
|
||
|
||
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
|
||
# Don't overwrite text that was corrected by Step 5c IPA fix
|
||
if not cell.get("_ipa_corrected"):
|
||
cell["text"] = _words_to_reading_order_text(filtered)
|
||
|
||
# Remove cells that became empty after bullet removal
|
||
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)
|
||
|
||
# 5j-pre. Remove cells whose text is entirely garbled / artifact noise.
|
||
# OCR on image areas produces short nonsensical fragments ("7 EN", "Tr",
|
||
# "\\", "PEE", "a=") that survive earlier filters because their rows also
|
||
# contain real content in other columns. Remove them here.
|
||
_COMMON_SHORT_WORDS = {
|
||
# German
|
||
"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",
|
||
# English
|
||
"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):
|
||
is_artifact = True
|
||
elif len(core) <= 2 and core.lower() not in _COMMON_SHORT_WORDS and not core.isalpha():
|
||
# Short non-alphabetic text like "a=", not word beginnings like "Zw"
|
||
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):
|
||
# Mixed digits + letters in short text (e.g. "7 EN", "a=3")
|
||
is_artifact = True
|
||
if is_artifact:
|
||
kept.append(None) # placeholder
|
||
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:
|
||
# Also remove rows that became completely empty
|
||
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)
|
||
|
||
# 5j. Normalise word_box order to reading order (group by Y, sort by X).
|
||
# The frontend renders colored cells from word_boxes array order
|
||
# (GridTable.tsx), so they MUST be in left-to-right reading order.
|
||
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]
|
||
# Check if order actually changed
|
||
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)
|
||
|
||
duration = time.time() - t0
|
||
|
||
# 6. Build result
|
||
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 from all word_boxes in cells
|
||
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 for faithful grid reconstruction
|
||
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))
|
||
|
||
# --- Dictionary detection on assembled grid ---
|
||
# Build lightweight ColumnGeometry-like structures from zone columns for
|
||
# dictionary signal scoring.
|
||
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
|
||
# Build pseudo-ColumnGeometry per column
|
||
pseudo_geoms = []
|
||
for col in zone_cols:
|
||
ci = col["index"]
|
||
col_cells = [c for c in zone_cells if c.get("col_index") == ci]
|
||
# Flatten word_boxes into word dicts compatible with _score_language
|
||
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),
|
||
})
|
||
# Fallback: use cell text if no word_boxes
|
||
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)
|
||
|
||
# --- Syllable divider insertion for dictionary pages ---
|
||
# Only on confirmed dictionary pages with article columns (der/die/das).
|
||
# The article_col_index check avoids false positives on synonym lists,
|
||
# word frequency tables, and other alphabetically sorted non-dictionary pages.
|
||
# Additionally, insert_syllable_dividers has its own pre-check for existing
|
||
# pipe characters in cells (OCR must have already found some).
|
||
syllable_insertions = 0
|
||
if (dict_detection.get("is_dictionary")
|
||
and dict_detection.get("article_col_index") is not None
|
||
and img_bgr is not None):
|
||
try:
|
||
from cv_syllable_detect import insert_syllable_dividers
|
||
syllable_insertions = insert_syllable_dividers(
|
||
zones_data, img_bgr, session_id,
|
||
)
|
||
except Exception as e:
|
||
logger.warning("Syllable insertion failed: %s", e)
|
||
|
||
# Clean up internal flags before returning
|
||
for z in zones_data:
|
||
for cell in z.get("cells", []):
|
||
cell.pop("_ipa_corrected", None)
|
||
|
||
result = {
|
||
"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"),
|
||
},
|
||
"duration_seconds": round(duration, 2),
|
||
}
|
||
|
||
return result
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Endpoints
|
||
# ---------------------------------------------------------------------------
|
||
|
||
@router.post("/sessions/{session_id}/build-grid")
|
||
async def build_grid(session_id: str):
|
||
"""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.
|
||
|
||
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)
|
||
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}/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
|