OCR

What it does and why it matters

OCR reads text from images. Take a photo of a document, and OCR converts the visual characters into digital text you can search, edit, and process. It's the foundation of document digitization. Without OCR, scanned documents and photos are just pictures. With OCR, they become actual text data.

The technology has been around for decades but has improved dramatically with deep learning. Early OCR struggled with anything but perfectly clear, standard fonts. Modern OCR handles handwriting, unusual fonts, skewed documents, poor lighting, and complex layouts. Accuracy rates above 99% are common for clean printed text. Even challenging documents produce usable results.

Use cases are everywhere. Digitizing paper archives. Reading receipts for expense tracking. Extracting text from screenshots. Processing mail and packages. Converting printed books to ebooks. Making image-based PDFs searchable. Banking apps that let you deposit checks by photo. Accessibility tools that read signs and menus to visually impaired users.

The workflow typically combines OCR with post-processing. Raw OCR output might have errors, so spell checking and format validation clean up results. For structured documents like forms and invoices, OCR pairs with layout analysis to understand which text belongs to which field. The combination of reading characters and understanding document structure is what makes modern document processing powerful.