⌘K
We use cookies to improve your experience
We use essential cookies to make our site work. With your consent, we may also use non-essential cookies to improve user experience.
Definition
OCR (Optical Character Recognition) is technology that converts images of text — such as scanned documents, photos of signs, or PDF pages — into machine-readable, editable text. Modern OCR uses neural networks and can handle multiple languages, handwriting, and complex layouts.
OCR works by analyzing the shapes and patterns in an image to identify individual characters. Traditional OCR used template matching and feature extraction, but modern systems use deep learning neural networks (typically convolutional neural networks) trained on millions of text samples. This enables recognition of diverse fonts, handwriting styles, and degraded text.
Tesseract, originally developed by HP and now maintained by Google, is the most widely used open-source OCR engine. It supports over 100 languages and can run in the browser via WebAssembly (Tesseract.js). Commercial OCR solutions from Google (Cloud Vision), Amazon (Textract), and Microsoft (Azure Computer Vision) offer higher accuracy on complex documents.
Common OCR applications include digitizing printed documents, extracting text from photos (receipts, business cards, signs), making scanned PDFs searchable, and automating data entry. OCR accuracy depends on image quality, font clarity, and language — clean printed text in common fonts achieves 99%+ accuracy, while handwriting and degraded documents may have significantly lower accuracy.