Two stage classifierfor Arabic Handwritten Character Recognition

Abstract

In this paper we will present a two phase method for isolated Arabic handwritten character recognition system. The new method combines two levels based on two classifiers, a public and a private according to the similar features among characters. In the first level, we built a public classifier to deal with all character groups, each group contains characters with overlapped feature. The public classifier classifies the characters in the SUST-ARG dataset (Sudan University for Sciences and Technology Arabic Recognition Group) to specified groups. In the second level, we created a private classifier for each group to recognize and classify the characters within a group. The system was applied to 34 Arabic characters and achieved 78.79% recognition rate for the tested dataset within the first level of the grouping model and achieved 93% recognition rate for the tested dataset using the two level models.