← AI Terminology
OCR / Document AI
OCR (optical character recognition) extracts text from images of documents; Document AI extends this to layout, tables, forms, and key-value understanding.
Essential infrastructure for enterprise automation and RAG over scans.
Essential infrastructure for enterprise automation and RAG over scans.
Why It Matters in AI
Most business knowledge sits in PDFs and scans. OCR/Document AI unlocks search, extraction, and LLM grounding on real paperwork — a high-ROI AI vertical.
Key Points
| Aspect | Description |
|---|---|
| OCR | Detect and read characters/words/lines |
| Use | Invoices, IDs, archives, RAG chunking |
| Layout | Reading order, blocks, tables, forms |
| Models | Classic Tesseract; modern layout transformers; VLMs |
| Quality | Noise, handwriting, multi-column pitfalls |
| Related | VLM, NER, information extraction |
Simple Analogy
A clerk who can type up what’s on a photographed form and also know which box is “address” versus “total.”
Common Usage Examples
- Tesseract/PaddleOCR pipelines
- LayoutLMv3 document understanding
- VLM OCR for complex pages
- Feed OCR text into RAG
Summary
In short: OCR and Document AI turn images of paperwork into structured machine text — the gateway from scans to searchable, automatable knowledge.