Handwritten Digit Recognition Using BP Neural Network

The handwritten digit recognition program based on BP Neural Network is a computer application developed using MATLAB. This program employs the Backpropagation Neural Network algorithm to recognize and classify handwritten digits. Through inputting image data of handwritten digits, the program can identify them as corresponding numerical values. The development process involves four key stages: data collection, data preprocessing, network training, and model evaluation. During data collection, numerous handwritten digit image samples are acquired and properly labeled and categorized. In the data preprocessing phase, image data undergoes noise removal, normalization, and feature extraction to optimize neural network training and recognition performance. Feature extraction typically involves techniques like image binarization and dimension reduction to convert pixel data into suitable input vectors for the neural network. The network training stage implements the BP Neural Network algorithm using MATLAB's neural network toolbox (e.g., feedforwardnet or patternnet functions). This involves configuring network parameters such as hidden layer architecture, learning rate, and activation functions to establish correlations between input patterns and corresponding digit outputs. The backpropagation algorithm adjusts connection weights through iterative forward propagation and error backward propagation cycles. Finally, during model evaluation, the trained neural network undergoes comprehensive testing and validation using separate test datasets. Performance metrics like recognition accuracy, confusion matrices, and error rates are calculated to determine the system's effectiveness in handwritten digit recognition. Key MATLAB functions for evaluation include confusionmat for confusion matrix generation and plotconfusion for visualization. This BP Neural Network-based handwritten digit recognition program provides practical insights into neural network applications for image recognition tasks, demonstrating core concepts like supervised learning, pattern recognition, and adaptive system training.