What is Optical Character Recognition?
Also known as: OCR
Optical Character Recognition (OCR) is a technology that converts images of printed or handwritten text, such as scanned documents or photos, into machine-readable and editable digital text.
OCR works by first preprocessing the image to enhance quality, such as removing noise or adjusting contrast. It then detects text regions and breaks them down into individual characters or words using pattern matching or machine learning models.
Modern OCR relies on deep learning techniques like convolutional neural networks to recognize characters accurately across different fonts, languages, and layouts. Post-processing steps often correct errors and format the output text.
Key ideas include feature extraction from images and sequence modeling to handle context, making OCR robust for real-world variations in lighting and text style.
Example
A mobile banking app uses OCR to scan a paper check, automatically extracting the account number, amount, and date to deposit funds without manual typing.
Why it matters
OCR powers document digitization at scale, enabling searchability in archives, automation in industries like finance and healthcare, and accessibility tools that convert printed text for screen readers.
Frequently asked questions
Accuracy depends on image quality, font clarity, and language support, but modern systems often exceed 95% on clean printed text.