Handwritten digit recognition based on DCT features and SVM classifier

Handwritten digit recognition is an active topic in optical character recognition applications and pattern learning research. However, the extraction of informative features from handwritten digits for recognition task remains the most important step for achieving high accuracy. This work investigates the effectiveness of four feature extraction approaches based on Discrete Cosine Transform (DCT) to capture discriminative features of handwritten Digits and compare it to classical PCA. These approaches are: DCT upper left corner(ULC) coefficients, DCT zigzag coefficients, block based DCT ULC coefficients and block based DCT zigzag coefficients. The coefficients of each DCT variant are used as input data for Support Vector Machine Classifier to evaluate their performances. The objective of this work is to identify the optimal feature extraction approach that speeds up the learning algorithms while maximizing the classification accuracy. The results have been analysed and compared in terms of classification accuracy and reduction rate and the findings have demonstrated that the block based DCT zigzag feature extraction yields a superior performance than its counterparts.