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Optical Character Recognition (OCR) is a technology that converts different types of text images—such as scanned paper documents, photos of signs, or image-only PDFs—into editable and machine-readable text data. Without OCR, a scanned document is just a static image file, but with OCR, the text can be searched, edited, copied, and processed by other software.

How OCR technology works. The process of OCR involves several key steps that transform an image into usable text:

Image acquisition: A scanner, camera, or other hardware is used to capture the image of the document and convert it into a digital bitmap.

Preprocessing: The software cleans up the image to improve accuracy by:

Deskewing: Correcting any tilt or alignment issues from the scanning process.

Binarization: Converting the image to black and white to distinguish text from the background.

Despeckling: Removing stray pixels or digital "noise" from the image.

Text recognition: The processed image is sent to the OCR engine, which uses one of two methods to recognize characters:

Pattern matching: Compares each character's shape, or "glyph," to a library of known font patterns. This works best with typed text in a known font.

Feature extraction: Analyzes a character by its structural features, such as curves, lines, and intersections. This advanced method, often using machine learning and neural networks, allows for greater accuracy across different fonts and handwriting styles.

Post-processing: The software reassembles the recognized characters into words, sentences, and paragraphs. Dictionaries and other language models are used to correct any errors and improve overall accuracy.