What OCR (Optical Character Recognition) means in practice
OCR is the step of converting an image (or PDF page rendered as an image) into machine-readable text. Modern OCR — Tesseract, PaddleOCR, AWS Textract, Azure Form Recognizer, Google Document AI — handles printed text reliably and handwritten text passably. The hard problems are downstream: text alone is not structured data. "4,28,940" on a row labelled "AMOUNT" needs to become an integer field tied to an invoice record. That work is IDP, not OCR. Treating OCR as the destination rather than the foundation is the most common reason enterprise document-automation programmes stall after the first wave of straight-through documents.
Related terms
Intelligent Document Processing (IDP) →
End-to-end automation of document workflows: capture, classify, extract structured data, validate, route, and integrate into downstream systems.
LLM-augmented extraction →
Using a large language model to extract structured fields from documents — particularly effective on layout-free documents where template-based OCR fails.
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Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.