Replaces position-based word matching with row-based sequence alignment
to fix doubled words and cross-line averaging in Kombi-Modus.
New algorithm:
1. Group words into rows by Y-position clustering
2. Match rows between engines by vertical center proximity
3. Within each row: walk both sequences left-to-right, deduplicating
4. Unmatched rows kept as-is
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Even after multi-criteria matching, near-duplicate words can slip through
(same text, centers within 30px horizontal / 15px vertical). The new
_deduplicate_words() removes these, keeping the higher-confidence copy.
Regression test with real session data (row 2 with 145 near-dupes)
confirms no duplicates remain after merge + deduplication.
Tests: 37 → 45 (added TestDeduplicateWords, TestMergeRealWorldRegression).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The merge algorithm now uses 3 criteria instead of just IoU > 0.3:
1. IoU > 0.15 (relaxed threshold)
2. Center proximity < word height AND same row
3. Text similarity > 0.7 AND same row
This prevents doubled overlapping words when both PaddleOCR and
Tesseract find the same word at similar positions. Unique words
from either engine (e.g. bullets from Tesseract) are still added.
Tests expanded: 19 → 37 (added _box_center_dist, _text_similarity,
_words_match tests + deduplication regression test).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
PaddleOCR als neue engine=paddle Option in der OCR-Pipeline.
Microservice auf Hetzner (paddleocr-service/), async HTTP-Client
(paddleocr_remote.py), Frontend-Dropdown, automatisch words_first.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix_cell_phonetics() ersetzt fehlerhafte IPA-Klammern UND fuegt fehlende
Lautschrift fuer englische Woerter ein (z.B. badge, film, challenge, profit).
Wird auf alle Zellen mit col_type column_en/column_text angewandt.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Spalten-, Zeilen-, Woerter-Overlay und alle nachfolgenden Steps
(LLM-Review, Rekonstruktion) lesen jetzt image/cropped mit Fallback
auf image/dewarped. Tests fuer page_crop.py hinzugefuegt (25 Tests).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
process-single-page now runs the full CV pipeline (deskew → dewarp → columns →
rows → cell-first OCR v2 → LLM review) for much better extraction quality.
Falls back to LLM vision if pipeline imports are unavailable.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Cell-First OCR (v2): Each cell is cropped and OCR'd in isolation,
eliminating neighbour bleeding (e.g. "to", "ps" in marker columns).
Uses ThreadPoolExecutor for parallel Tesseract calls.
Document type detection: Classifies pages as vocab_table, full_text,
or generic_table using projection profiles (<2s, no OCR needed).
Frontend dynamically skips columns/rows steps for full-text pages.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Frontend: Replace hardcoded EN/DE/Example vocab table with unified dynamic
table driven by columns_used from backend. Labeling, confirmation, counts,
and summary badges are now all cell-based instead of branching on isVocab.
Backend: Change _cells_to_vocab_entries() entry filter from checking only
english/german/example to checking ANY mapped field. This preserves rows
with only marker or source_page content, fixing the issue where marker
sub-columns disappeared at the end of OCR processing.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Header/footer words (page numbers, chapter titles) could pollute the
left-edge alignment bins and trigger false sub-column splits. Now
_detect_header_footer_gaps() runs early and its boundaries are passed
to _detect_sub_columns() to filter those words from clustering and
the split threshold check.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Word 'left' values in ColumnGeometry.words are relative to the content
ROI (left_x), but geo.x is in absolute image coordinates. The split
position was computed from relative word positions and then compared
against absolute geo.x, resulting in negative widths and no splits on
real data. Pass left_x through to _detect_sub_columns to bridge the
two coordinate systems.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replace gap-based splitting with alignment-bin approach: cluster word
left-edges within 8px tolerance, find the leftmost bin with >= 10% of
words as the true column start, split off any words to its left as a
sub-column. This correctly handles both page references ("p.59") and
misread exclamation marks ("!" → "I") even when the pixel gap is small.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Detects hidden sub-columns (e.g. page references like "p.59") within
already-recognized columns by clustering word left-edge positions and
splitting when a clear minority cluster exists. The sub-column is then
classified as page_ref and mapped to VocabRow.source_page.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Gaps that extend to the image boundary (top/bottom edge) are not valid
content separators — they typically represent dewarp padding. Only gaps
with content on both sides qualify as header/footer boundaries.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Check for actual ink content in detected top/bottom regions:
- 'header'/'footer' when text is present (e.g. title, page number)
- 'margin_top'/'margin_bottom' when the region is empty page margin
Also update all skip-type sets and color maps for the new types.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replace the trivial top_y/bottom_y threshold check with horizontal
projection gap analysis that finds large whitespace gaps separating
header/footer content from the main body. This correctly detects
headers (e.g. "VOCABULARY" banners) and footers (page numbers) even
when _find_content_bounds includes them in the content area.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Thin black lines (1-5px) at page edges from scanning were incorrectly
detected as content, shifting content bounds and creating spurious
IGNORE columns. This filters narrow projection runs (<1% of image
dimension) and introduces explicit margin_left/margin_right regions
for downstream page reconstruction.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
1. Unit tests: 76 new parametrized tests for noise filter, phonetic detection,
cell text cleaning, and row merging (116 total, all green)
2. Continuation-row merge: detect multi-line vocab entries where text wraps
(lowercase EN + empty DE) and merge into previous entry
3. Empty DE fallback: secondary PSM=7 OCR pass for cells missed by PSM=6
4. Batch-OCR: collect empty cells per column, run single Tesseract call on
column strip instead of per-cell (~66% fewer calls for 3+ empty cells)
5. StepReconstruction UI: font scaling via naturalHeight, empty EN/DE field
highlighting, undo/redo (Ctrl+Z), per-cell reset button
6. Session reprocess: POST /sessions/{id}/reprocess endpoint to re-run from
any step, with reprocess button on completed pipeline steps
Also fixes pre-existing dewarp_image tuple unpacking bug in run_cv_pipeline
and updates dewarp tests to match current (image, info) return signature.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
