Two fixes:
1. Always use min-channel for color images instead of gated comparison
that was falling back to standard grayscale (which has only 23%
contrast for white-on-green VIN stickers).
2. Add grayscale-only OCR path (CLAHE + denoise, no thresholding)
between adaptive and Otsu attempts. Tesseract's LSTM engine is
designed to handle grayscale input directly and often outperforms
binarized input where thresholding creates artifacts.
Pipeline order: adaptive threshold → grayscale-only → Otsu threshold
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>
