Neural Network-Based Character Recognition

This document introduces a neural network-based approach for character recognition, which represents a classic application of pattern recognition technology with diverse practical implementations. The source code specifically trains a Backpropagation (BP) neural network to recognize 26 English letters through supervised learning. The implementation includes key components such as: input layer normalization for character image preprocessing, hidden layer configuration with activation functions, and output layer design for multi-class classification. The training process utilizes gradient descent optimization with error backpropagation to adjust synaptic weights. While demonstrating English alphabet recognition, this methodology can be extended to other character sets including digits and Chinese characters through appropriate dataset adaptation. Furthermore, recognition accuracy can be enhanced through techniques like network architecture optimization, advanced training algorithms (e.g., Levenberg-Marquardt), and data augmentation strategies. Ultimately, neural network-based character recognition proves to be a robust technical solution with broad applications across multiple domains including document digitization, automated data entry, and intelligent character recognition systems.