MATLAB Source Code for Offline Handwritten Character Recognition

This MATLAB source code package implements offline handwritten character recognition, featuring comprehensive modules for feature extraction, Bayes classifier, K-nearest neighbor classification, and nearest neighbor classification. The implementation includes several core functions: TestScriptRecognition.m serves as the main test script that coordinates the entire recognition pipeline. ScriptFeaExtract.m handles feature extraction, typically implementing techniques like zoning features, projection histograms, or gradient features to convert handwritten images into numerical feature vectors. KNearestEstimate.m implements the K-nearest neighbor algorithm, which classifies characters by finding the K most similar training samples in the feature space. NearestEstimate.m provides the nearest neighbor classifier, a special case of KNN where K=1. BayesTrain.m performs training for the Bayes classifier, calculating prior probabilities and likelihood functions from training data. Bayes.m implements the Bayes classification algorithm that applies Bayes' theorem for probabilistic classification. CrossValidate.m conducts m-fold cross-validation to evaluate model performance and prevent overfitting. Offline handwritten character recognition refers to computer-based recognition of pre-written characters (as opposed to real-time recognition). This MATLAB implementation provides a complete framework for understanding and applying character recognition techniques in research and practical applications. The codebase demonstrates fundamental pattern recognition algorithms and their application to handwritten character classification. Through these source files, researchers and developers can gain deeper insights into offline handwritten character recognition technology and apply these methods to character recognition research and practical implementations. The comprehensive implementation covers the entire workflow from feature extraction to classification and validation, making it suitable for educational purposes and experimental development.