Text Detection and Recognition

Detecting and recognizing text in images is a common task performed in computer vision applications. For example, you can capture video of a road scene from a moving vehicle, recognize signposts in the captured scene, and alert the driver about the signs.

You can combine detection and recognition combined into a two-step process, where the first step finds regions that contain text, and then the second step recognizes the text within the regions.

Text detection algorithms use local image features, machine learning or deep learning, to locate or segment text within an image. The examples in the Computer Vision Toolbox™ demonstrate how to use blob analysis, the maximally stable extremal regions (MSER) feature detector, and the character region awareness for text detection (CRAFT) deep learning model for text detection.

Once you have detected the text, text recognition models, based on machine learning or deep learning, process the text regions to return the predicted text. The ocr function uses pretrained language models to recognize text in multiple languages. You can also train a custom language model using the trainOCR function. For more information, see Getting Started with OCR.