Extracting text and tabular data from scanned invoices involves OCR (Optical Character Recognition) and post-processing to structure the data. Here's a breakdown of the process and considerations for your project:
Steps to Extract Text and Tabular Data
OCR for Text Extraction:
Use tools like Tesseract, Google Vision API, or AWS Textract to
convert scanned invoices into machine-readable text.
These tools often provide bounding box coordinates, useful for
extracting structured data like tables.
Preprocessing:
- Image enhancement: Improve OCR accuracy by preprocessing images (e.g., noise removal, resizing, binarization).
- Invoice segmentation: Separate text into sections like headers, tables, and footers.
Tabular Data Extraction:
- Use OCR tools that support table recognition, such as AWS Textract or Adobe Document Cloud.
- Alternatively, apply grid-detection techniques (e.g., Hough Line Transform or contour detection with OpenCV) to locate tables and extract rows/columns.
Data Parsing:
- Post-process OCR output to match the required structure.
- Use regular expressions, NLP libraries, or custom logic to extract fields and map data correctly.
Export to Excel:
- Use libraries like Pandas or OpenPyXL in Python to structure and export data to Excel.
Model Considerations
Whether to use one model or multiple depends on the similarity across invoice types:
Single Model:
If invoice types share similar layouts or contain common key-value pairs and table structures, you can train a single model using tools like LayoutLMv3 or fine-tuned transformers designed for document processing.
Use labeled datasets to train the model to recognize different sections and adapt to slight variations.
Multiple Models:
If invoice formats vary significantly (e.g., different languages, table placements, or no standard layout), it may be practical to create multiple models or rule-based pipelines tailored to specific invoice types.
Recommended Approach
Start with a Single Model:
- Use a pre-trained document processing model like LayoutLM or Donut (OCR-free).
- Fine-tune the model with a diverse dataset containing samples of all 25 invoice types.
Add Custom Pipelines:
- Supplement with custom logic for outliers or invoice types that deviate significantly from the norm.
- Use key-value pair extraction for text-heavy invoices and table detection algorithms for data-heavy invoices.
Iterate:
- Evaluate model performance across all invoice types.
- Adjust or split into smaller pipelines/models only if the single model fails to generalize.
Tools to Consider
- OCR: Tesseract, Google Vision, AWS Textract, Adobe Document Cloud
- Table Extraction: Camelot, Tabula, PyPDF2, OpenCV
- Document AI: LayoutLMv3, Donut, Google Document AI
- Excel Integration: Pandas, OpenPyXL, XlsxWriter