Applications

OCR (Optical Character Recognition)

Extracting machine-readable text and structure from images, scans, and PDFs.

OCR has moved far beyond text extraction. Modern document AI stacks (Azure Document Intelligence, Google Document AI, AWS Textract, and open models like Docling and Nougat) recover text, tables, key-value pairs, and reading order from complex layouts.

For unstructured documents — invoices, contracts, medical records, forms — OCR is the first step in a pipeline that typically ends with an LLM extracting structured fields and validating them against business rules.

Accuracy on printed English documents now exceeds 99%; handwriting, low-quality scans, and non-Latin scripts still require domain-tuned models and human-in-the-loop review.

Key points

  • Table extraction is usually the hardest part of the pipeline
  • Layout-aware OCR beats plain text extraction for forms and contracts
  • Combining OCR + LLM structured extraction is the current best practice
  • Confidence scores must gate automated actions

Common use cases

Invoice and PO processing
KYC and identity verification
Medical records digitization
Contract analysis and abstraction

Frequently asked

Related terms

Computer Vision

The field of AI that enables machines to interpret and act on visual information from images and video.

Large Language Model (LLM)

A neural network trained on massive text corpora to understand and generate human-like language.

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