Strip SQLAlchemy dialect prefix from DATABASE_URL for asyncpg.
Set search_path via server_settings on pool creation.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Strategic pivot: Studio-v2 becomes a language learning platform.
Compliance guardrail added to CLAUDE.md — no scan/OCR of third-party
content in customer frontend. Upload of OWN materials remains allowed.
Phase 1.1 — vocabulary_db.py: PostgreSQL model for 160k+ words
with english, german, IPA, syllables, examples, images, audio,
difficulty, tags, translations (multilingual). Trigram search index.
Phase 1.2 — vocabulary_api.py: Search, browse, filters, bulk import,
learning unit creation from word selection. Creates QA items with
enhanced fields (IPA, syllables, image, audio) for flashcards.
Phase 1.3 — /vocabulary page: Search bar with POS/difficulty filters,
word cards with audio buttons, unit builder sidebar. Teacher selects
words → creates learning unit → redirects to flashcards.
Sidebar: Added "Woerterbuch" (/vocabulary) and "Lernmodule" (/learn).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
qwen2.5vl:32b needs ~100GB RAM and crashes Ollama.
llama3.2-vision:11b is already installed and fits in memory.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New module vision_ocr_fusion.py: Sends scan image + OCR word
coordinates + document type to Qwen2.5-VL 32B. The LLM reads
the image visually while using OCR positions as structural hints.
Key features:
- Document-type-aware prompts (Vokabelseite, Woerterbuch, etc.)
- OCR words grouped into lines with x/y coordinates in prompt
- Low-confidence words marked with (?) for LLM attention
- Continuation row merging instructions in prompt
- JSON response parsing with markdown code block handling
- Fallback to original OCR on any error
Frontend (admin-lehrer Grid Review):
- "Vision-LLM" checkbox toggle
- "Typ" dropdown (Vokabelseite, Woerterbuch, etc.)
- Steps 1-3 defaults set to inactive
Activate: Check "Vision-LLM", select document type, click "OCR neu + Grid".
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New endpoint POST /sessions/{id}/rerun-ocr-and-build-grid that:
1. Runs scan quality assessment
2. Applies CLAHE enhancement if degraded (controlled by enhance toggle)
3. Re-runs dual-engine OCR (RapidOCR + Tesseract) with min_conf filter
4. Merges OCR results and stores updated word_result
5. Builds grid with max_columns constraint
Frontend: Orange "OCR neu + Grid" button in GridToolbar.
Unlike "Neu berechnen" (which only rebuilds grid from existing words),
this button re-runs the full OCR pipeline with quality settings.
Now CLAHE toggle actually has an effect — it enhances the image
before OCR runs, not after.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The max_columns parameter was only implemented in cv_words_first.py
(vocab-worksheet path) but NOT in _build_grid_core which is what
the admin OCR Kombi pipeline uses. The Kombi pipeline uses
grid_editor_helpers._cluster_columns_by_alignment() which has its
own column detection.
Fix: Post-processing step 5k merges narrowest columns after grid
building when zone has more columns than max_columns. Cells from
merged columns get their text appended to the target column.
min_conf word filtering was already working (applied before grid build).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Dev-only toggles belong in admin-lehrer (port 3002) only.
The customer frontend runs the pipeline with optimal defaults
and shows only the finished results.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Each quality improvement step can now be toggled independently:
- CLAHE checkbox (Step 3: image enhancement on/off)
- MaxCols dropdown (Step 2: 0=unlimited, 2-5)
- MinConf dropdown (Step 1: auto/20/30/40/50/60)
Backend: Query params enhance, max_cols, min_conf on process-single-page.
Response includes active_steps dict showing which steps are enabled.
Frontend: Toggle controls in VocabularyTab above the table.
This allows empirical A/B testing of each step on the same scan.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Step 1: scan_quality.py — Laplacian blur + contrast scoring, adjusts
OCR confidence threshold (40 for good scans, 30 for degraded).
Quality report included in API response + shown in frontend.
Step 2: max_columns parameter in cv_words_first.py — limits column
detection to 3 for vocab tables, preventing phantom columns D/E
from degraded OCR fragments.
Step 3: ocr_image_enhance.py — CLAHE contrast + bilateral filter
denoising + unsharp mask, only for degraded scans (gated by
quality score). Pattern from handwriting_htr_api.py.
Frontend: quality info shown in extraction status after processing.
Reprocess button now derives pages from vocabulary data.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Types from deleted ocr-pipeline/types.ts inlined into ocr-kombi/types.ts.
All imports updated across components/ocr-kombi/ and components/ocr-pipeline/.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Two bugs fixed:
1. reprocessPages() failed silently after session resume because
successfulPages was empty. Now derives pages from vocabulary
source_page or selectedPages as fallback.
2. process-single-page endpoint built vocabulary entries WITHOUT
applying merge logic (_merge_wrapped_rows, _merge_continuation_rows).
Now applies full merge pipeline after vocabulary extraction.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Allows reprocessing pages from the vocabulary view to apply
new merge logic without navigating back to page selection.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
When textbook authors wrap text within a cell (e.g. long German
translations), OCR treats each physical line as a separate row.
New _merge_wrapped_rows() detects this by checking if the primary
column (EN) is empty — indicating a continuation, not a new entry.
Handles: empty EN + DE text, empty EN + example text, parenthetical
continuations like "(bei)", triple wraps, comma-separated lists.
12 tests added covering all cases.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Phase 3.2 — MicrophoneInput.tsx: Browser Web Speech API for
speech-to-text recognition (EN+DE), integrated for pronunciation practice.
Phase 4.1 — Story Generator: LLM-powered mini-stories using vocabulary
words, with highlighted vocab in HTML output. Backend endpoint
POST /learning-units/{id}/generate-story + frontend /learn/[unitId]/story.
Phase 4.2 — SyllableBow.tsx: SVG arc component for syllable visualization
under words, clickable for per-syllable TTS.
Phase 4.3 — Gamification system:
- CoinAnimation.tsx: Floating coin rewards with accumulator
- CrownBadge.tsx: Crown/medal display for milestones
- ProgressRing.tsx: Circular progress indicator
- progress_api.py: Backend tracking coins, crowns, streaks per unit
Also adds "Geschichte" exercise type button to UnitCard.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New feature: After OCR vocabulary extraction, users can generate interactive
learning modules (flashcards, quiz, type trainer) with one click.
Frontend (studio-v2):
- Fortune Sheet spreadsheet editor tab in vocab-worksheet
- "Lernmodule generieren" button in ExportTab
- /learn page with unit overview and exercise type cards
- /learn/[unitId]/flashcards — Flip-card trainer with Leitner spaced repetition
- /learn/[unitId]/quiz — Multiple choice quiz with explanations
- /learn/[unitId]/type — Type-in trainer with Levenshtein distance feedback
- AudioButton component using Web Speech API for EN+DE TTS
Backend (klausur-service):
- vocab_learn_bridge.py: Converts VocabularyEntry[] to analysis_data format
- POST /sessions/{id}/generate-learning-unit endpoint
Backend (backend-lehrer):
- generate-qa, generate-mc, generate-cloze endpoints on learning units
- get-qa/mc/cloze data retrieval endpoints
- Leitner progress update + next review items endpoints
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The tokenizer regex only matches alphabetic characters, so text
before the first word match (like "(= " in "(= I won...") was
silently dropped when reassembling the corrected text.
Now preserves text[:first_match_start] as a leading prefix.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Instead of keeping only specific symbols (_KEEP_SYMBOLS), now only
removes explicitly decorative symbols (_REMOVE_SYMBOLS: > < ~ \ ^ etc).
All other punctuation (= ( ) ; : - etc.) is preserved by default.
This is more robust: any new symbol used in textbooks will be kept
unless it's in the small block-list of known decorative artifacts.
Fixes: (= token still being removed on page 5 despite being in
the allow-list (possibly due to Unicode variants or whitespace).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Rule (a2) in Step 5i removed word_boxes with no letters/digits as
"graphic OCR artifacts". This incorrectly removed = signs used as
definition markers in textbooks ("film = 1. Film; 2. filmen").
Added exception list _KEEP_SYMBOLS for meaningful punctuation:
= (= =) ; : - – — / + • · ( ) & * → ← ↔
The root cause: PaddleOCR returns "film = 1. Film; 2. filmen" as one
block, which gets split into word_boxes ["film", "=", "1.", ...].
The "=" word_box had no alphanumeric chars and was removed as artifact.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
= signs are used as definition markers in textbooks ("film = 1. Film").
They were incorrectly removed by two filters:
1. grid_build_core.py Step 5j-pre: _PURE_JUNK_RE matched "=" as
artifact noise. Now exempts =, (=, ;, :, - and similar meaningful
punctuation tokens.
2. cv_ocr_engines.py _is_noise_tail_token: "pure non-alpha" check
removed trailing = tokens. Now exempts meaningful punctuation.
Fixes: "film = 1. Film; 2. filmen" losing the = sign,
"(= I won and he lost.)" losing the (=.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Words like "probieren)" or "Englisch)" were incorrectly flagged as
gutter OCR errors because the closing parenthesis wasn't stripped
before dictionary lookup. The spellchecker then suggested "probierend"
(replacing ) with d, edit distance 1).
Two fixes:
1. Strip trailing/leading parentheses in _try_spell_fix before checking
if the bare word is valid — skip correction if it is
2. Add )( to the rstrip characters in the analysis phase so
"probieren)" becomes "probieren" for the known-word check
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Text like "Betonung auf der 1. Silbe: profit ['profit]" was
incorrectly detected as garbled IPA and replaced with generated
IPA transcription of the previous row's example sentence.
Added guard: if the cell text contains >=3 recognizable words
(3+ letter alpha tokens), it's normal text, not garbled IPA.
Garbled IPA is typically short and has no real dictionary words.
Fixes: Row 13 C3 showing IPA instead of pronunciation hint text.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Multi-line cells (containing \n) that don't already start with a
bullet character get • prepended in the frontend. This ensures
bullet points are visible regardless of whether the backend inserted
them (depends on when boxes were last rebuilt).
Skips header rows and cells that already have •, -, or – prefix.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Revert row expansion — multi-line bullet cells stay as single cells
with \n and text-wrap (tb='2'). This way the text reflows when the
user resizes the column, like normal Excel behavior.
Row height auto-scales by line count (24px * lines).
Vertical alignment: top (vt=0) for multi-line cells.
Removed leading-space indentation hack (didn't work reliably).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Hardcodierte REGULATIONS/INDUSTRIES/INDUSTRY_REGULATION_MAP durch
JSON-Import ersetzt. 320 Dokumente in 17 Kategorien mit collapsible
Sektionen pro doc_type. page.tsx von 3672 auf 2655 Zeilen reduziert.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Multi-line cells (\n): expanded into separate rows so each line gets
its own cell. Continuation lines (after •) indented with leading spaces.
Bullet marker lines (•) are bold.
Font-size detection: cells with word_box height >1.3x median get bold
and larger font (fs=12) for box titles.
Headers: is_header rows always bold with light background tint.
Box borders: thick colored outside border + thin inner grid lines.
Content zone: light gray grid borders.
Auto-fit column widths from longest text per column.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Column widths now calculated from the longest text in each column
(~7.5px per character + padding). Takes the maximum of auto-fit
width and scaled original pixel width.
Multi-line cells: uses the longest line for width calculation.
Spanning header cells excluded from width calculation (they span
multiple columns and would inflate single-column widths).
Minimum column width: 60px.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Height: sheet height auto-calculated from row count (26px/row + toolbar),
no more cutoff at 21 rows. Row count set to exact (no padding).
Box borders: thick colored outside border + thin inner grid lines.
Content zone: light gray grid lines on all cells.
Headers: bold (bl=1) for is_header rows. Larger font detected via
word_box height comparison (>1.3x median → fs=12 + bold).
Box cells: light tinted background from box_bg_hex.
Header cells in boxes: slightly stronger tint.
Multi-line cells: text wrap enabled (tb='2'), \n preserved.
Bullet points (•) and indentation preserved in cell text.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Each zone becomes its own Excel sheet tab with independent column widths:
- Sheet "Vokabeln": main content zone with EN/DE/example columns
- Sheet "Pounds and euros": Box 1 with its own 4-column layout
- Sheet "German leihen": Box 2 with single column for flowing text
This solves the column-width conflict: boxes have different column
widths optimized for their content, which is impossible in a single
unified sheet (Excel limitation: column width is per-column, not per-cell).
Sheet tabs visible at bottom (showSheetTabs: true).
Box sheets get colored tab (from box_bg_hex).
First sheet active by default.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Install @fortune-sheet/react (MIT, v1.0.4) as Excel-like spreadsheet
component. New SpreadsheetView.tsx converts unified grid data to
Fortune Sheet format (celldata, merge config, column/row sizes).
StepAnsicht now has Spreadsheet/Grid toggle:
- Spreadsheet mode: full Fortune Sheet with toolbar (bold, italic,
color, borders, merge cells, text wrap, undo/redo)
- Grid mode: existing GridTable for quick editing
Box-origin cells get light tinted background in spreadsheet view.
Colspan cells converted to Fortune Sheet merge format.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Backend (unified_grid.py):
- build_unified_grid(): merges content + box zones into one zone
- Dominant row height from median of content row spacings
- Full-width boxes: rows integrated directly
- Partial-width boxes: extra rows inserted when box has more text
lines than standard rows fit (e.g., 7 lines in 5-row height)
- Box-origin cells tagged with source_zone_type + box_region metadata
Backend (grid_editor_api.py):
- POST /sessions/{id}/build-unified-grid → persists as unified_grid_result
- GET /sessions/{id}/unified-grid → retrieve persisted result
Frontend:
- GridEditorCell: added source_zone_type, box_region fields
- GridTable: box-origin cells get tinted background + left border
- StepAnsicht: split-view with original image (left) + editable
unified GridTable (right). Auto-builds on first load.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Content sections: use dominant (median) row height from all content
rows instead of per-section average. This ensures uniform row height
above and below boxes (the standard case on textbook pages).
Box sections: distribute height proportionally by text line count
per row. A header (1 line) gets 1/7 of box height, a bullet with
3 lines gets 3/7. Fixes Box 2 where row 3 was cut off because
even distribution didn't account for multi-line cells.
Removed overflow:hidden from box container to prevent clipping.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Content rows were incorrectly filtered out when their Y overlapped
with a box, even if the box only covered the right half of the page.
Now checks both Y AND X overlap — rows are only excluded if they
start within the box's horizontal range.
Fixes: rows next to Box 2 (lend, coconut, taste) were missing from
reconstruction because Box 2 (x=871, w=525) only covers the right
side, but left-side content rows at x≈148 were being filtered.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Major rewrite of reconstruction rendering:
- Page split into vertical sections (content/box) around box boundaries
- Content sections: uniform row height = (last_row - first_row) / (n-1)
- Box sections: rows evenly distributed within box height
- Content rows positioned absolutely at original y-coordinates
- Font size derived from row height (55% of row height)
- Multi-line cells (bullets) get expanded height with indentation
- Boxes render at exact bbox position with colored border
- Preparation for unified grid where boxes become part of main grid
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replace manual word_box positioning (wild/unsnapped) with the
server-rendered words-overlay image from the OCR step endpoint.
This shows the same cleanly snapped red letters as the OCR step.
Endpoint: /sessions/{id}/image/words-overlay
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Font: use font_size_suggestion_px * scale directly (removed 0.85 factor)
- Row height: calculate from row-to-row spacing (y_min of next row
minus y_min of current row) instead of text height (y_max - y_min).
This produces correct line spacing matching the original layout.
- Multi-line cells: height multiplied by line count
Content zone should now span from ~250 to ~2050 matching the original.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Left panel: Original scan + OCR word overlay (red text at exact
word_box positions) + coordinate grid
Right panel: Reconstructed layout + same coordinate grid
Features:
- Coordinate grid toggle with 50/100/200px spacing options
- Grid lines labeled with pixel coordinates in original image space
- Both panels share the same scale for direct visual comparison
- OCR overlay shows detected text in red mono font at original positions
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New pipeline step showing the reconstructed page with all zones
positioned at their original coordinates:
- Content zones with vocabulary grid cells
- Box zones with colored borders (from structure detection)
- Colspan cells rendered across multiple columns
- Multi-line cells (bullets) with pre-wrap whitespace
- Toggle to overlay original scan image at 15% opacity
- Proportionally scaled to viewport width
- Pure CSS positioning (no canvas/Fabric.js)
Pipeline: 14 steps (0-13), Ground Truth moved to Step 13.
Added colspan field to GridEditorCell type.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Chained ternary (colored ? div : multiline ? textarea : input) caused
webpack SWC parser issues. Replaced with IIFE {(() => { if/return })()}
which is more robust and readable.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Remove extra curly braces around the textarea/input ternary that
caused webpack syntax error. The ternary is now a chained condition:
hasColoredWords ? <div> : text.includes('\n') ? <textarea> : <input>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Benjamin Admin