Handwritten Digit Recognition with MATLAB

This project implements a MATLAB program for handwritten digit classification using the minimum Euclidean distance method. The dataset is obtained from UCI Machine Learning Repository, containing extensive samples of handwritten digits. The classification algorithm calculates Euclidean distances between test samples and training prototypes, assigning each digit to the class with the smallest distance. The implementation demonstrates how to compute key performance metrics including accuracy, recall, and F1-score for comprehensive evaluation. Through this program, users can gain practical understanding of digit recognition algorithms, examine classification effectiveness, and explore potential optimizations in pattern recognition systems. Key implementation aspects include: - Data preprocessing and normalization techniques - Euclidean distance calculation using vectorized operations - Classification decision logic based on minimum distance criteria - Performance metric computation using confusion matrix analysis - Modular code structure for easy experimentation and modification