Evidential combination of SVM classifiers for writer recognition

This paper addresses the problem of writer identification from handwritten documents. We propose a new approach for offline writer identification based on a combination of SVM classifiers. The main contribution of this study is to propose a combination module using Dempster-Shafer Theory (DST) in an attempt to improve the overall system performance. DST is an effective theoretical framework to treat uncertainty and imprecision related to information sources. The evaluation of the proposed system was carried on different publicly available databases on Arabic and Latin scripts. Experimental results reveal that the proposed combination approach outperforms the conventional combination methods and achieves interesting results as compared to those reported by the existing writer recognition systems.