Ensemble classifier construction for Arabic handwritten recongnition

Abstract

Handwritten recognition is a very active research domain that led to several works in the literature for the Latin Writing. The current systems tendency is oriented toward the classifiers combination and the integration of multiple information sources. In this paper, we describe an approach based on diversity measures for Arabic handwritten recognition using optimized Multiple classifier system. The aim of this paper is to study Arabic handwriting recognition using the optimization of MCS based on diversity measures. This approach selects the best classifier subset from a large set of classifiers taking into account different diversity measures. The experimental results presented are encouraging and open other perspectives in the domain of classifiers selection especially speaking for Arabic Handwritten word recognition.