OCR (Optical Character Recognition) technology converts images containing text, such as scanned documents, photos of documents, or PDFs, into editable and searchable machine-encoded text. It works by analyzing the patterns of light and dark in the document image to identify shapes that correspond to letters, numbers, and symbols, essentially "reading" the text from the picture. This differs fundamentally from just viewing the scanned image, which is a static picture you cannot edit or search through as text.

In practice, businesses widely use OCR to digitize paper records such as invoices, receipts, contracts, and forms into editable text. For example, an accounting department might scan paper invoices and use OCR to extract vendor names, dates, and amounts automatically into their accounting software. Libraries and archives also employ OCR extensively to convert historical documents or printed books into accessible digital text files. Common tools for OCR include dedicated software like Adobe Acrobat, built-in features in scanning apps, and online services like Google Drive (open a PDF image or image file in Google Docs).

OCR offers significant efficiency gains by enabling document searchability, editing, and automated data extraction, saving considerable manual effort. However, its accuracy depends heavily on scan quality; poor resolution, smudges, unusual fonts, or complex layouts can lead to errors needing manual review. Future developments focus on AI-powered OCR that handles diverse layouts and handwriting better. While using cloud-based OCR services offers convenience, it's crucial to consider the privacy implications of sending sensitive documents to external platforms. Despite limitations, OCR remains a foundational tool for digitization efforts.