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 .