8938661850
Features: - Auto-labeling pipeline: CSV values -> PDF search -> YOLO annotations - Flexible date matching: year-month match, nearby date tolerance - PDF text extraction with PyMuPDF - OCR support for scanned documents (PaddleOCR) - YOLO training and inference pipeline - 7 field types: InvoiceNumber, InvoiceDate, InvoiceDueDate, OCR, Bankgiro, Plusgiro, Amount Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
130 lines
3.1 KiB
YAML
130 lines
3.1 KiB
YAML
# Invoice Master POC v2 Configuration
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# Default configuration for invoice field extraction system
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# PDF Processing
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pdf:
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dpi: 300 # Resolution for rendering PDFs to images
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min_text_chars: 30 # Minimum chars to consider PDF as text-based
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# OCR Settings
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ocr:
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engine: paddleocr # OCR engine to use
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lang: en # Language code (en, sv, ch, etc.)
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use_gpu: false # Enable GPU acceleration
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# Field Normalization
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normalize:
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# Bankgiro formats
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bankgiro:
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format: "XXXX-XXXX" # Standard 8-digit format
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alternatives:
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- "XXX-XXXX" # 7-digit format
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# Plusgiro formats
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plusgiro:
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format: "XXXXXXX-X" # Standard format with check digit
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# Amount formats
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amount:
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decimal_separator: "," # Swedish uses comma
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thousand_separator: " " # Space for thousands
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currency_symbols:
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- "SEK"
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- "kr"
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# Date formats
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date:
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output_format: "%Y-%m-%d"
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input_formats:
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- "%Y-%m-%d"
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- "%Y-%m-%d %H:%M:%S"
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- "%d/%m/%Y"
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- "%d.%m.%Y"
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# Field Matching
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matching:
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min_score_threshold: 0.7 # Minimum score to accept match
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context_radius: 100 # Pixels to search for context keywords
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# Context keywords for each field (Swedish)
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context_keywords:
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InvoiceNumber:
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- "fakturanr"
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- "fakturanummer"
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- "invoice"
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InvoiceDate:
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- "fakturadatum"
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- "datum"
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InvoiceDueDate:
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- "förfallodatum"
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- "förfaller"
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- "betalas senast"
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OCR:
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- "ocr"
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- "referens"
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Bankgiro:
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- "bankgiro"
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- "bg"
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Plusgiro:
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- "plusgiro"
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- "pg"
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Amount:
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- "att betala"
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- "summa"
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- "total"
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- "belopp"
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# YOLO Training
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yolo:
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model: yolov8s # Model architecture (yolov8n/s/m/l/x)
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epochs: 100
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batch_size: 16
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img_size: 1280 # Image size for training
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# Data augmentation
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augmentation:
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rotation: 5 # Max rotation degrees
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scale: 0.2 # Scale variation
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mosaic: 0.0 # Disable mosaic for documents
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hsv_h: 0.0 # No hue variation
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hsv_s: 0.1 # Slight saturation variation
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hsv_v: 0.2 # Brightness variation
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# Class definitions
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classes:
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0: invoice_number
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1: invoice_date
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2: invoice_due_date
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3: ocr_number
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4: bankgiro
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5: plusgiro
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6: amount
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# Auto-labeling
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autolabel:
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min_confidence: 0.7 # Minimum score to include in training
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bbox_padding: 0.02 # Padding around bboxes (fraction of image)
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# Dataset Split
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dataset:
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train_ratio: 0.8
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val_ratio: 0.1
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test_ratio: 0.1
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random_seed: 42
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# Inference
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inference:
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confidence_threshold: 0.5 # Detection confidence threshold
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iou_threshold: 0.45 # NMS IOU threshold
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enable_fallback: true # Enable regex fallback if YOLO fails
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fallback_min_missing: 2 # Min missing fields to trigger fallback
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# Paths (relative to project root)
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paths:
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raw_pdfs: data/raw_pdfs
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images: data/images
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labels: data/labels
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structured_data: data/structured_data
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models: models
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reports: reports
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