Using 2DLDA feature extraction in Handwritten Persian/Arabic Digit Recognition

Abstract

The main goal in majority of handwriting digit recognition systems is to extract a vector feature for every digit in order to distinguish the digits and classify them in their real classes. In this paper, we propose three different feature extraction methods with kNN classifier for Handwritten Persian/Arabic Digit Recognition. Experiments on real world datasets indicate 2DLDA can provide a solution with improved quality in terms of classification accuracy and computation time performance in contrast to two other methods, PCA and PCA+LDA.