Digit Recognition Using BP Neural Network Implementation

This documentation presents a MATLAB implementation of a Backpropagation (BP) neural network designed for digit recognition of numerals 0-9 in various font styles. The system employs a multi-layer perceptron architecture with sigmoid activation functions, utilizing gradient descent optimization for weight updates during training. Key components include image preprocessing routines for digit normalization, feature extraction modules that convert input images to feature vectors, and a configurable network topology allowing adjustments to hidden layer neurons. We provide a pre-trained network file in compressed format, containing optimized weight matrices and bias values that significantly reduce training time while maintaining recognition accuracy. The implementation includes error backpropagation algorithms with momentum term integration to prevent local minima convergence, alongside cross-validation mechanisms for performance evaluation.