Optical Character Recognition (OCR) extracts text from images and documents using AI. Modern OCR capabilities: Deep learning achieves 99%+ accuracy on printed text, handles multiple fonts/languages, extracts structured data from documents. Technologies: Tesseract (Google, open source, 100+ languages), EasyOCR (PyTorch-based, 80+ languages), PaddleOCR (excellent multilingual), Document AI services (AWS Textract, Google Document AI, Azure Form Recognizer). Beyond basic OCR: Document understanding extracts tables, forms, hierarchies. Named entity recognition identifies key information. Layout analysis preserves structure. Challenges: Handwriting recognition still difficult, degraded documents need preprocessing, complex layouts require specialized models. Preprocessing pipeline: Deskewing, denoising, binarization, contrast enhancement improve accuracy. Use cases: Digitizing archives, automating data entry, invoice processing, receipt scanning, accessibility (screen readers), searchable PDF creation. Best practices: Use appropriate resolution (300 DPI+), clean images before processing, validate critical extractions, train custom models for domain-specific documents.