Why OCR-only IDP fails in production (and how AI-powered IDP fixes it)

The post argues that traditional OCR-based intelligent document processing often struggles in production because real business documents commonly include handwriting, multi-page tables, changing layouts, and both native digital and scanned formats. It presents four cases where conventional page-by-page OCR and template-driven extraction can create errors or require manual intervention: interpreting handwritten notes and stamps, combining line items across page breaks, extracting fields from variable supplier layouts, and unnecessarily converting text-based PDFs into images for OCR. Vertesia positions its AI-powered approach, based on vision-enabled large language models and semantic document understanding, as an alternative that processes visual and native text signals together, recognizes document-wide structures, maps fields by meaning rather than fixed coordinates, and selects extraction methods based on file type. The post attributes these differences to modern AI-native architecture rather than legacy OCR systems retrofitted with semantic capabilities, and recommends evaluating IDP products using complex, unstructured documents rather than polished examples.

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