Handwritten Chinese Character Recognition Based on Convolutional Neural Network - MATLAB Version with 509 Character Categories

This is a MATLAB implementation of handwritten Chinese character recognition based on convolutional neural networks (CNN). The system can recognize 509 categories of handwritten Chinese characters, encompassing both commonly used characters and rare characters. The implementation utilizes deep learning algorithms, including convolutional neural networks for feature extraction and backpropagation algorithms for model optimization, to achieve higher accuracy and faster recognition speed. The system architecture includes multiple convolutional layers for automatic feature learning, pooling layers for dimensionality reduction, and fully connected layers for classification. The implementation employs cross-entropy loss function and gradient descent optimization to train the network effectively. Additionally, the system features adaptive learning capabilities, allowing continuous model adjustment and optimization based on new user-input samples through incremental training procedures. This functionality enhances recognition accuracy over time by incorporating new character variations and writing styles. The code implementation includes data preprocessing modules for image normalization, data augmentation techniques for improving model generalization, and evaluation metrics for performance assessment. Key MATLAB functions used in the implementation include image processing toolbox for character preprocessing and Deep Learning Toolbox for neural network construction and training. Overall, this system serves as a reliable Chinese character recognition tool applicable to various scenarios such as natural language processing, OCR systems, and document digitization projects. The modular code structure allows for easy integration into larger applications and customization for specific character recognition requirements.