Two-Dimensional Matrix Principal Component Analysis Useful for Character Recognition

Low dimensional feature representation with enhanced discriminatory power is of paramount importance to any recognition systems. Principal component analysis (PCA) is a classical feature extraction and data representation technique widely used in the area of pattern recognition and computer vision. In this paper, two-dimensional Principal Component Analysis (2D-PCA) is presented for character image representation. 2D-PCA is based on 2D image matrices rather than 1D vectors so that image matrix does not need to be transform into a vector prior to feature extraction as done in PCA. Experimental results on character database (Printed and Handwritten) showed a good recognition rate compared to other existing methods.