OCR Text Recognition

This article explores text recognition implementation using MATLAB, a powerful programming language and development environment widely used for image processing and computer vision applications. In today's digital era, OCR technology plays a vital role in converting physical documents like books and newspapers into editable digital formats for efficient storage and management. MATLAB-based text recognition enables accurate conversion of printed text into digital text through systematic image processing workflows, facilitating subsequent data analysis and manipulation.

The MATLAB implementation involves three key phases: First, image preprocessing using MATLAB's Image Processing Toolbox functions like imadjust for contrast enhancement, imbinarize for thresholding, and edge detection algorithms such as Canny edge detection (edge function). Second, optical character recognition using MATLAB's OCR engine (ocr function) which employs feature extraction and machine learning algorithms to recognize characters from preprocessed images. Third, post-processing text analysis using MATLAB's text handling capabilities (string arrays and regular expressions) for formatting and data extraction.

Beyond document digitization, MATLAB's OCR framework supports diverse applications including handwritten text recognition (using customized training datasets with the trainOCR function), license plate recognition, and real-time text extraction from video streams. These techniques underpin modern applications like smartphone document scanning and intelligent character recognition in IoT devices. With continuous advancements in deep learning integration (through Deep Learning Toolbox), MATLAB-based OCR solutions are evolving toward higher accuracy and broader implementation scenarios.