Explore MATLAB source code curated for "手写数字识别" with clean implementations, documentation, and examples.
Implementation of handwritten digit recognition using Fisher Linear Discriminant Analysis on MATLAB platform. This method achieves automated digit classification through sample data projection and distance calculation. Similar to Bayesian methods, it requires test subjects to follow specific writing conventions with standardized forms and moderate writing speed for optimal performance.
Implementation of handwritten digit recognition using PCA in MATLAB, involving preprocessing of raw images followed by digit recognition through traditional PCA, improved PCA algorithm, and 2D PCA methodology with code-focused explanations.
Original implementation of Support Vector Machine for handwritten digit recognition achieving 100% accuracy rate with optimized parameter tuning and kernel function selection
Implementation of KNN algorithm for 0-9 handwritten digit classification achieving over 90% accuracy, featuring separate training (7291 samples) and testing (2791 samples) datasets. The MATLAB-based solution includes comprehensive documentation with clear code structure and algorithm explanations.
MATLAB implementation of a BP neural network designed for handwritten digit recognition, highly practical and ready to execute with immediate results demonstration
The attached files test0-9 are generated after executing MATLAB code. Taking test0 (980x784) as an example, each row represents a grayscale image of a digit. Execution allows visualization of each image's appearance and characteristics.
Handwritten digit recognition is a specialized branch of Optical Character Recognition (OCR) technology that focuses on developing computer algorithms to automatically identify human-written Arabic numerals on paper. The system typically involves processing sample images through preprocessing techniques, feature extraction methods, and provides error metric curves for neural network training evaluation, often visualized using libraries like Matplotlib or TensorBoard.
A self-developed handwritten digit recognition system featuring machine learning algorithms and digital image processing - feedback and suggestions welcome
This project offers a MATLAB class implementation of Convolutional Neural Networks (CNNs), originally developed by Yann LeCun. The implementation demonstrates practical applications including handwritten digit recognition, face detection, and robot navigation through layered architecture with convolution, pooling, and fully connected layers.
Backpropagation Neural Network for Handwritten Digit Recognition with Support for Digits 0-99