Ensemble classifier composition: Impact on feature based offline cursive character recognition

In this paper we propose different ensemble classifier compositions and investigate their influence on offline cursive character recognition. Cursive characters are difficult to recognize due to different handwriting styles of different writers. The recognition accuracy can be improved by training an ensemble of classifiers on multiple feature sets focussing on different aspects of character images. Given the feature sets and base classifiers, we have developed multiple ensemble classifier compositions using three architectures. Type-1 architecture is based on homogeneous base classifiers and Type-2 architecture is composed of heterogeneous base classifiers. Type-3 architecture is based on hierarchical fusion of decisions. The experimental results demonstrate that the presented method with best composition of classifiers and feature sets performs better than existing methods for offline cursive character recognition.