OFF-LINE HANDWRITTEN WORD RECOGNITION USING ENSEMBLE OF CLASSIFIER SELECTION AND FEATURES FUSION
Nabiha Azizi, Nadir Farah, Mokhtar Sellami · 2010
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 two approaches for Arabic handwritten recognition using optimized Multiple classifier system MCS . The first rests on cooperation and selection of feature set in MCS studying the effect of fusion methods on global system performance The second one used Diversity measures and individual accuracy classifier for selecting the best set of classifier; its 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 10 different classifiers, we then test the effect of: 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 domain of classifiers selection especially speaking for Arabic Handwritten word recognition.