New cv_color_detect.py module:
- detect_word_colors(): annotates existing words with text color (HSV analysis)
- recover_colored_text(): finds colored text regions missed by standard OCR
(e.g. red ! markers) using HSV masks + contour detection
Integrated into build-grid: words get color/color_name fields, recovered
colored regions are merged into the word list before grid building.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Only cluster left-edges of words that begin a new group within their row
(first word or preceded by a large gap). This filters out mid-phrase
word positions (IPA transcriptions, second words in multi-word entries)
that were causing too many false columns.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Column detection now clusters word left-edges by X-proximity and filters
by row coverage (Y-coverage), matching the proven approach from cv_layout.py
but using precise OCR word positions instead of ink-based estimates.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Backend: new grid_editor_api.py with build-grid endpoint that detects
bordered boxes, splits page into zones, clusters columns/rows per zone
from Kombi word positions. New DB column grid_editor_result JSONB.
Frontend: GridEditor component with editable HTML tables per zone,
column bold toggle, header row toggle, undo/redo, keyboard navigation
(Tab/Enter/Arrow), image overlay verification, and save/load.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Since ocr_region_paddle() now runs RapidOCR locally (same PP-OCRv5 models),
the "PaddleOCR (Hetzner)" labels were misleading. Renamed to "PP-OCRv5 (lokal)".
Removed the Kombi-Vergleich tab since both sides would produce identical results.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
RapidOCR uses the same PP-OCRv5 ONNX models locally, avoiding 504 timeouts
from remote PaddleOCR on large images. Set FORCE_REMOTE_PADDLE=1 to bypass.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add /rapid-kombi backend endpoint using local RapidOCR + Tesseract merge,
KombiCompareStep component for parallel execution and side-by-side overlay,
and wordResultOverride prop on OverlayReconstruction for direct data injection.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Backend: Add spatial overlap check (>=50% horizontal IoU) to Kombi merge
so words at the same position are deduplicated even when OCR text differs.
Frontend: Add yPct/hPct to WordPosition so each word renders at its actual
vertical position instead of all words collapsing to the cell center Y.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
PaddleOCR returns entire phrases as single boxes (e.g. "More than 200
singers took part in the"). The merge algorithm compared word-by-word
but Paddle had multi-word boxes vs Tesseract's individual words, so
nothing matched and all Tesseract words were added as "extras" causing
duplicates. Now splits Paddle boxes into individual words before merge.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
PaddleOCR 3.4.0 removed 'latin' language support. Use 'en' with
explicit ocr_version='PP-OCRv5' instead, with fallback for older API.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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>
Runs both OCR engines on the preprocessed image and merges results:
word boxes matched by IoU, coordinates averaged by confidence weight.
Unmatched Tesseract words (bullets, symbols) are added for better coverage.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The slide positioning hook was re-matching cell.text tokens against
word_boxes via fuzzy text similarity, which broke positioning for
special characters (!, bullet points, IPA). Now uses word_box
coordinates directly — exact OCR positions without re-interpretation.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When PaddleOCR returns "!Betonung" as a single word box, the overlay
positions text starting at the "!" instead of the actual word. Split
such boxes into ["!", "Betonung"] with proportional position splitting,
matching the existing IPA bracket splitting logic.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replaces custom _paddle_words_to_grid_cells with the proven
build_grid_from_words from cv_words_first.py — same function the
regular pipeline uses with PaddleOCR. Handles phrase splitting,
column clustering, and produces cells with word_boxes that the
slide/cluster positioning hooks expect.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
One cell per row with all words as word_boxes instead of one cell per
word. Gives OverlayReconstruction a row-spanning bbox_pct for correct
font sizing and per-word positions for slide/cluster placement.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Uses the cropped/dewarped image instead of the original so the overlay
shows the correctly oriented page. 5 steps instead of 2.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
New 2-step mode (Upload → PaddleOCR+Overlay) alongside the existing
7-step pipeline. Backend endpoint runs PaddleOCR on the original image
and clusters words into rows/cells directly. Frontend adds a mode
toggle and PaddleDirectStep component.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace sequential 1:1 token-to-box mapping with fuzzy text matching.
Each token from cell.text finds its best matching word_box by text
similarity (normalized prefix match + substring bonus). Handles:
- Reordered boxes (different sort between text and boxes)
- IPA corrections changing token boundaries
- Token/box count mismatches
Unmatched tokens get interpolated positions from matched neighbors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
PaddleOCR returns "badge[bxd3]" without space, but the IPA fixer
produces "badge [bˈædʒ]" with space, creating a token count mismatch
between cell.text and word_boxes. Now also split at "[" boundaries
so each IPA bracket gets its own sub-box.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
PaddleOCR returns phrase-level bounding boxes (e.g. "competition
[kompa'tifn]" as one box) but the overlay slide mechanism expects
one box per word for accurate positioning. Multi-word boxes are now
split proportionally by character count with small gaps between words.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
words_first was storing word_boxes in percent coordinates while
cv_cell_grid.py uses absolute pixel coordinates. The overlay slide
mechanism divides by imgW to get percentages, so percent-in-percent
caused positions near zero. Now both grid builders use the same format.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When engine=paddle is selected, the backend overrides grid_method to
words_first and returns plain JSON (no SSE streaming). The frontend
was not aware of this override — it sent stream=true and tried to parse
SSE events from a JSON response, resulting in "Keine Daten".
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Bilder > 1500px werden vor dem Upload verkleinert. Koordinaten
werden zurueckskaliert. JPEG statt PNG fuer schnelleren Upload.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Update chunk counts for 8 successfully ingested DE laws (Phase H1)
- Add 6 new BGB-Teile entries (AGB, Fernabsatz, Kaufrecht, Widerruf, Digital)
- Add EGBGB Widerrufsbelehrung entry
- Update COLLECTION_TOTALS: gesetze 58304→63567 (+5263 Phase H chunks)
- Add Verbraucherschutz thematic group to Landkarte
- Extend ecommerce industry map with consumer protection regulations
- Update date to March 2026
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>
Add 15 new regulations from Phase H ingestion:
- DE: PAngV, VSBG, ProdHaftG, VerpackG, ElektroG, BattDG, BFSG, UWG, GewO
- EU: Warenkauf-RL, Klausel-RL, UGP-RL, Preisangaben-RL, Omnibus-RL, BattVO
Chunk counts set to 0 (will be updated after successful ingestion).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
_has_ipa_gap() prüft ob Tesseract eine IPA-Klammer übersehen hat anhand
des physischen Abstands zwischen Headword und nächstem Wort. Ohne Gap
(z.B. "be good at sth.", "Focus on language") wird kein IPA eingefügt.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
_insert_missing_ipa ueberspringe Texte mit >6 Woertern oder Klammern.
Neue _insert_headword_ipa fuer column_text: prueft nur das erste Wort
der Zeile, unabhaengig von Textlaenge oder vorhandenen Klammern.
Ausserdem _sync_word_boxes_after_ipa_insert gefixt: Token-Vergleich
nutzt jetzt paralleles Durchlaufen statt zip (verschobene Positionen).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Fuer column_text werden fehlende IPA-Lautschriften (challenge, profit,
film, badge) wieder eingefuegt, aber gleichzeitig eine synthetische
word_box erzeugt, damit die 1:1 Token-zu-Box Zuordnung im Overlay
erhalten bleibt.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
_strip_orphan_bracket entfernte deutsche Bedeutungsangaben in Klammern,
weil sie weder als Grammar-Partikel noch als IPA erkannt wurden.
Fix: Klammerinhalte mit echten Wörtern (>=4 Buchstaben) werden behalten.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
word_boxes wurden nur im Cell-Crop-Pfad (narrow columns) gesetzt,
aber nicht im Full-Page Word-Assignment-Pfad (broad columns).
Jetzt werden die Tesseract-Wort-Koordinaten in beiden Pfaden gespeichert.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
TEXT kommt aus cell.text (bereinigt, IPA-korrigiert).
POSITIONEN kommen aus word_boxes (exakte OCR-Koordinaten).
Tokens werden 1:1 in Leserichtung zugeordnet.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Backend: _ocr_cell_crop speichert jetzt word_boxes mit exakten
Tesseract/RapidOCR Wort-Koordinaten (left, top, width, height)
im Cell-Ergebnis. Absolute Bildkoordinaten, bereits zurueckgemappt.
Frontend: Slide-Hook nutzt word_boxes direkt wenn vorhanden —
jedes Wort wird exakt an seiner OCR-Position platziert. Kein
Pixel-Scanning noetig. Fallback auf alten Slide wenn keine Boxes.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Gruppen-Sliding schob nicht weit genug nach rechts. Zurueck zum
Original-Einzelwort-Slide, aber mit den Fixes:
- fontRatio=1.0 (konsistente Schriftgroesse wie Fallback)
- Token-Breiten aus medianCh * 0.7 / refFontSize (statt totalInk)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Vorher: split(/\s+/) zerlegte alles in Einzelwoerter, verlor die
Spaltenstruktur (3+ Spaces zwischen Gruppen). Woerter stauten sich links.
Jetzt: split(/\s{3,}/) erhält Gruppen wie im Cluster-Modus. Jede Gruppe
wird als Einheit von links nach rechts geschoben bis Tinte gefunden.
Breite = max(gemessene Textbreite, tatsaechliche Tintenbreite).
fontRatio=1.0, kein Wort geht verloren.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fontRatio war 0.65 (35% kleiner als Fallback-Rendering). Jetzt 1.0
wie beim Fallback. Token-Breiten berechnet aus measureText skaliert
auf die tatsaechlich gerenderte Schriftgroesse (medianCh * 0.7).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Schriftgroesse wird jetzt GLOBAL aus der medianen Zellhoehe berechnet
(65% der Zellhoehe als Ziel-Font). Alle Tokens bekommen dieselbe
konsistente Groesse. Die Slide-Logik bestimmt nur noch die x-Position.
Vorher: Scale pro Zelle aus Ink-Span/Textbreite -> inkonsistente Groessen.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
totalInk zaehlte nur dunkle Pixel-Spalten (Striche), ignorierte
Luecken zwischen Buchstaben. Scale war dadurch viel zu klein,
Schrift unlesbar. Jetzt wird der Ink-Span (erstes bis letztes
dunkles Pixel) als Referenz fuer die Textbreite verwendet.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Neuer Hook useSlideWordPositions: Schiebt alle erkannten Woerter von links
nach rechts ueber die Pixel-Projektion bis jedes Wort auf seiner Tinte
einrastet. Kein Wort geht verloren, keine Cluster-Matching-Regeln noetig.
Toggle-Button (Slide/Cluster) in der Overlay-Toolbar zum Umschalten.
Bestehender Cluster-Algorithmus bleibt als Alternative erhalten.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
