文本检测和识别
检测和识别图像中的文本是在计算机视觉应用中执行的一项常见任务。例如，您可以从行驶的车辆中捕获道路场景的视频，识别所捕获场景中的路标，并提醒驾驶员注意这些符号。
您可以将检测和识别合并为一个两步过程，其中，第一步是找到包含文本的区域，而第二步是识别区域中的文本。
文本检测算法使用局部图像特征、机器学习或深度学习来定位或分割图像中的文本。Computer Vision Toolbox™ 中的示例说明如何使用斑点分析、最大稳定极值区域 (MSER) 特征检测器和文本检测字符区域感知 (CRAFT) 深度学习模型进行文本检测。
您一旦检测到文本，文本识别模型就会基于机器学习或深度学习处理文本区域以返回预测的文本。ocr 函数使用预训练的语言模型来识别多种语言的文本。您还可以使用 trainOCR 函数来训练自定义语言模型。有关详细信息，请参阅Getting Started with OCR。
App
| 图像标注器 | 为计算机视觉应用标注图像 |
函数
主题
快速入门
- 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.
精选示例
Recognize Text Using Optical Character Recognition (OCR)
Recognize text in images using optical character recognition.
Recognize Seven-Segment Digits Using OCR
Use OCR to recognize seven-segmented digits in text detected by CRAFT and region properties.
Segment and Read Text in Image
Automatically detect and recognize text in images using MSER and OCR.
Automatically Detect and Recognize Text Using Pretrained CRAFT Network and OCR
Perform text recognition by using a deep learning based text detector and OCR.
Automate Ground Truth Labeling for OCR
Automate the labeling of text for OCR training and evaluation.
Train an OCR Model to Recognize Seven-Segment Digits
Train an OCR model that can recognize seven-segment numerals.
Digit Classification Using HOG Features
Classify digits using HOG features and a multiclass SVM classifier.
