Using Diversity in Classifier Set Selection for Arabic Handwritten Recognition

The first observation concerning Arabian manuscript reveals the complexity of the task, especially for the used classifiers ensemble. One of the most important steps in the design of a multi-classifier system (MCS), is the its components choice (classifiers). This step is very important to the overall MCS performance since the combination of a set of identical classifiers will not outperform the individual members. To select the best classifier set from a pool of classifiers, the classifier diversity is the most important property to be considered. The aim of this paper is to study Arabic handwriting recognition using MCS optimization based on diversity measures. The first approach selects the best classifier subset from large classifiers set taking into account different diversity measures. The second one chooses among the classifier set the one with the best performance and adds it to the selected classifiers subset. The performance in our approach is calculated using three diversity measures based on correlation between errors. On two database sets using 9 different classifiers, we then test the effect of using the criterion to be optimized (diversity measures,), and fusion methods (voting, weighted voting and Behavior Knowledge Space). The experimental results presented are encouraging and open other perspectives in the classifiers selection field especially speaking for Arabic Handwritten word recognition.