MATLAB Implementation of Optical Character Recognition (OCR) System

This project presents a comprehensive MATLAB implementation of Optical Character Recognition (OCR) technology. OCR is a sophisticated technique that converts textual content within images into editable and searchable text format, with applications spanning image processing, document management systems, and automation workflows. The implementation employs MATLAB's Image Processing Toolbox for initial image preprocessing, including noise reduction, contrast enhancement, and binarization using functions like imread(), imbinarize(), and medfilt2(). The core recognition engine utilizes pattern matching algorithms and machine learning approaches, potentially incorporating feature extraction methods such as HOG (Histogram of Oriented Gradients) and classification techniques via template matching or neural networks. Key implementation components include: - Image preprocessing pipeline for optimal character segmentation - Character isolation and normalization algorithms - Feature extraction modules for character identification - Post-processing routines including spell-checking and layout analysis The system includes sophisticated post-processing stages that enhance recognition accuracy through contextual analysis and error correction mechanisms. Users can leverage this implementation to perform efficient OCR tasks, extracting textual data from various image formats (JPEG, PNG, TIFF) for subsequent data processing, analysis, and integration into larger systems. The modular architecture allows for customization of recognition parameters and integration with additional MATLAB toolboxes for expanded functionality.