Learning OCR: From Images to Text

Definition and purpose of optical character recognition

Optical character recognition (OCR) is the electronic or mechanical conversion of images of typed, handwritten, or printed text into machine-encoded text. The input can come from scanned documents, photos of documents, scene images (such as text on signs), or subtitle text embedded in video. OCR is widely used to digitize printed text so it can be electronically edited, searched, stored more compactly, displayed online, and processed by other computer systems. The purpose of OCR is to enable reliable data entry and information extraction from visual text sources. It supports many real-world workflows such as processing passports, invoices, bank statements, receipts, business cards, and other printed records, and it is also used for tasks like making scanned documents searchable (e.g., searchable PDFs) and extracting text for downstream applications such as machine translation, text-to-speech, key data extraction, and text mining. OCR is a research area within pattern recognition, artificial intelligence, and computer vision, and modern systems can achieve high accuracy across many fonts and image formats.

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OCR applications in data entry and digitization

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