Handwritten Digit Recognition Based on Fisher Linear Discriminant Analysis

Handwritten digit recognition based on Fisher Linear Discriminant Analysis is implemented using the MATLAB platform. The method achieves automatic classification of handwritten digits by calculating sample data projections and projection distances. The MATLAB implementation typically involves extracting digit features using image preprocessing techniques, followed by applying Fisher's criterion to maximize between-class scatter while minimizing within-class scatter through eigenvalue decomposition. While this method shows significant improvement compared to traditional statistical learning approaches for handwritten digit recognition, it still presents certain limitations. The algorithm requires relatively strict writing standards from test subjects, necessitating standardized digit forms and moderate writing speeds. Furthermore, its adaptability to digit variety and deformation remains constrained, struggling with highly variable handwritten digit patterns. From an implementation perspective, the MATLAB code would commonly include functions for data normalization, covariance matrix calculation, and projection vector optimization using MATLAB's built-in matrix operations. To address these limitations, incorporating advanced machine learning algorithms such as deep learning architectures could enhance recognition accuracy and robustness. Potential enhancements could involve convolutional neural networks (CNNs) implemented through MATLAB's Deep Learning Toolbox, which would better handle digit variations through hierarchical feature learning.