Handwritten Digit Recognition Using PCA

In this article, we present a comprehensive approach to handwritten digit recognition using Principal Component Analysis (PCA). Initially, we perform image preprocessing in MATLAB, which includes normalization and noise reduction techniques to enhance input quality for digit classification. We then implement traditional PCA methodology, where we compute eigenvectors and eigenvalues from the covariance matrix of flattened image vectors to reduce dimensionality while preserving critical features. However, our analysis reveals limitations in this approach regarding sensitivity to lighting variations and spatial distortions. To address these issues, we develop an improved PCA algorithm incorporating adaptive weighting mechanisms and class-specific covariance adjustments through MATLAB's matrix operations and statistical工具箱 functions. This enhancement significantly boosts recognition accuracy by accounting for inter-class variations. Finally, we explore 2D PCA implementation, which directly processes image matrices without vectorization—utilizing MATLAB's efficient 2D covariance computations and singular value decomposition (SVD) to preserve spatial relationships. Our experimental results demonstrate that while standard PCA achieves solid baseline performance, the improved PCA and 2D PCA algorithms yield higher accuracy by better handling image structural information and reducing information loss during dimensionality reduction.