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.
Training and Evaluation
|Train OCR model to recognize text in image|
|Evaluate OCR results against ground truth|
|Store OCR quality metrics|
|Options for training OCR model|
|Create training data for OCR from ground truth|
|Detect texts in images by using CRAFT deep learning model|
|Detect MSER features|
|Properties of connected regions|
|Extract histogram of oriented gradients (HOG) features|
|Recognize text using optical character recognition|
|Store OCR results|
|Start installer to download, install, or uninstall Computer Vision Toolbox data|
- Getting Started with OCR
Detect and recognize text in multiple languages, train OCR models to recognize custom text.
- Train Custom OCR Model
Train an optical character recognition (OCR) model to recognize custom text.
- Install OCR Language Data Files
Support files for optical character recognition (OCR) languages.
- Local Feature Detection and Extraction
Learn the benefits and applications of local feature detection and extraction.
- Point Feature Types
Choose functions that return and accept points objects for several types of features.