Recognition of 10 Handwritten Digits Using BP Neural Network

This document addresses the problem of handwritten digit recognition. To solve this challenge, we designed a BP neural network-based model that effectively achieves accurate identification of 10 handwritten digits through systematic implementation. The model architecture incorporates multiple computational components including image preprocessing techniques (such as normalization and noise reduction), feature extraction methods (like gradient features or Fourier descriptors), and classification algorithms employing backpropagation learning. Key implementation aspects involve setting network parameters (hidden layers, learning rate), using activation functions (sigmoid/ReLU), and training with gradient descent optimization. Through comprehensive consideration of these technical factors, we successfully accomplished precise digit recognition. This research holds significant implications for advancing digital recognition technology and contributes positively to enhancing practical applications in artificial intelligence domains.