freight-doc-processor

You can extract data from POD and BOL freight documents using OCR by passing PDF or image file paths to the processing pipeline. The system identifies documents, parses key fields like bol_number and delivery dates, and matches them to loads.

Yes, you can automate matching BOL documents to loads by extracting OCR fields and verifying delivery details. The system surfaces discrepancies and missing PODs, enabling quick settlement actions and triggering invoicing workflows.

Yes, the document processing pipeline supports both PDFs and image attachments. It uses dependencies like pdfplumber and Pillow to handle OCR extraction across different file formats from email attachments.

The best way to automate freight document detection for invoicing is using an OCR and NLP pipeline that identifies POD and BOL attachments, extracts critical fields, and verifies load details to trigger automated settlement workflows.

Yes, you need Tesseract OCR and Docling installed along with other dependencies like pdfplumber and pytesseract. These tools provide the foundational OCR and document parsing capabilities required for robust error handling.

OCR can fail to extract fields from low quality BOL scans due to poor image resolution or skewed text. The pipeline uses ImageMagick and ocrmypdf to preprocess images, improving extraction accuracy from degraded freight documents.