Replace std-based channel selection (which incorrectly picked green for
green-tinted VIN stickers) with per-pixel min(B,G,R). White text stays
255 in all channels while colored backgrounds drop to their weakest
channel value, giving 2x contrast improvement. Add morphological
opening after thresholding to remove noise speckles from car body
surface that were confusing Tesseract's page segmentation.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
White text on green VIN stickers has only ~12% contrast in standard
grayscale conversion because the green channel dominates luminance.
The new _best_contrast_channel method evaluates each RGB channel's
standard deviation and selects the one with highest contrast, giving
~2x improvement for green-tinted VIN stickers. Falls back to standard
grayscale for neutral-colored images.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Save original, adaptive, and Otsu preprocessed images to
/tmp/vin-debug/{timestamp}/ when LOG_LEVEL is set to debug.
No images saved at info level. Volume mount added for access.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace filesystem-based debug system (VIN_DEBUG_DIR) with standard
logger.debug() calls that flow through Loki when LOG_LEVEL=DEBUG.
Use .env.logging variable for OCR LOG_LEVEL. Increase image capture
quality to 0.95 for better OCR accuracy.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implement async PDF processing for owner's manuals with maintenance
schedule extraction:
- Add PDF preprocessor with PyMuPDF for text/scanned PDF handling
- Add maintenance pattern matching (mileage, time, fluid specs)
- Add service name mapping to maintenance subtypes
- Add table detection and parsing for schedule tables
- Add manual extractor orchestrating the complete pipeline
- Add POST /extract/manual endpoint for async job submission
- Add Redis job queue support for manual extraction jobs
- Add progress tracking during processing
Processing pipeline:
1. Analyze PDF structure (text layer vs scanned)
2. Find maintenance schedule sections
3. Extract text or OCR scanned pages at 300 DPI
4. Detect and parse maintenance tables
5. Normalize service names and extract intervals
6. Return structured maintenance schedules with confidence scores
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implement receipt-specific OCR extraction for fuel receipts:
- Pattern matching modules for date, currency, and fuel data extraction
- Receipt-optimized image preprocessing for thermal receipts
- POST /extract/receipt endpoint with field extraction
- Confidence scoring per extracted field
- Cross-validation of fuel receipt data
- Unit tests for all pattern matchers
Extracted fields: merchantName, transactionDate, totalAmount,
fuelQuantity, pricePerUnit, fuelGrade
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
OCR Service (Python/FastAPI):
- POST /extract for synchronous OCR extraction
- POST /jobs and GET /jobs/{job_id} for async processing
- Image preprocessing (deskew, denoise) for accuracy
- HEIC conversion via pillow-heif
- Redis job queue for async processing
Backend (Fastify):
- POST /api/ocr/extract - authenticated proxy to OCR
- POST /api/ocr/jobs - async job submission
- GET /api/ocr/jobs/:jobId - job polling
- Multipart file upload handling
- JWT authentication required
File size limits: 10MB sync, 200MB async
Processing time target: <3 seconds for typical photos
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Add Python-based OCR service container (mvp-ocr) as the 6th service:
- Python 3.11-slim with FastAPI/uvicorn
- Tesseract OCR with English language pack
- pillow-heif for HEIC image support
- opencv-python-headless for image preprocessing
- Health endpoint at /health
- Unit tests for health, HEIC support, and Tesseract availability
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
