Holistic Recognition of Online Handwritten Words Based on an Ensemble of SVM Classifiers

Abstract

In this paper, we present our recent study of a data driven approach to combining multiple SVM classifiers with RBF kernels each being trained with a distinct feature vector. The SVM classifiers in our ensemble are ranked based on their increasing order of average performance on the validation sample sets. The outputs of the SVM classifiers are combined based on a weighted average strategy which uses the above ranks of the underlying SVMs to determine the respective weights. In the present study, we design four sets of different feature vectors representing online handwritten words. Simple concatenation of these feature vectors does not help much in improving the recognition accuracy compared to the best performing feature vector among the four. Thus, we train distinct SVM classifiers with different feature vectors and combine their outputs at the final stage. The proposed recognition strategy is implemented on a limited vocabulary recognition problem of unconstrained mixed cursive online handwritten Bangla words. It improves existing recognition accuracies on a moderately large database of similar word samples.