From static to dynamic ensemble of classifiers selection: Application to Arabic handwritten recognition

Arabic handwriting word recognition is a challenging problem due to Arabic’s connected letter forms, consonantal diacritics and rich morphology. One way to improve the recognition rates classification task is to improve the accuracy of individual classifiers; another, is to apply ensemble of classifiers methods. To select the best classifier set from a pool of classifiers, the classifier diversity is considered one of the most important properties in static classifier selection. However, the advantage of dynamic ensemble selection versus static classifier selection is that used classifier set depends critically on the test pattern. In this paper, we propose two approaches for Arabic handwriting recognition (AHR) based on static and dynamic ensembles of classifiers selection. The first one selects statically the best set of classifiers from a pool of classifier already designed based on diversity measures. The second one represents a new algorithm based on Dynamic Ensemble of Classifiers Selection using Local Reliability measure (DECS-LR). It chooses the most confident ensemble of classifiers to label each test sample dynamically. Such a level of confidence is measured by calculating the proposed local reliability measure using confusion matrixes constructed during training level. We show experimentally that both approaches provide encouraging results with the second one leading to a better recognition rate for (AHR) system using IFN ENIT database.