Digital Recognition Using BP Neural Network with Implementation Details

I developed a digit recognition program based on BP (Backpropagation) neural network architecture that can identify hand-drawn digital images through algorithmic processing. The implementation begins with importing hand-drawn digit images into the system, followed by data preprocessing stages including image normalization, noise reduction, and feature extraction to enhance recognition accuracy. The core algorithm utilizes supervised learning where the network processes these optimized images through forward propagation, calculates error using mean squared error function, and performs backward propagation to adjust weights through gradient descent optimization. During training, the system iteratively updates neuron connection weights across multiple epochs until achieving high recognition accuracy thresholds. The fully trained neural network subsequently enables rapid and accurate digit classification for new input images, demonstrating effective pattern recognition capabilities through matrix operations and activation functions like sigmoid or ReLU in the hidden layers.