Offline writer identification in tamil using bagged classification trees

Abstract

In this paper, we explore the effectiveness of bagged classification trees, in solving the writer identification problem in the Tamil language. Unlike other languages, in Tamil the writer identification problem is mostly an unexplored problem. Novel feature extraction methods tailored to better understand Tamil characters have been proposed. The feature extraction methods used in this paper are chosen after analysing the statistical spread of a feature across different handwriting classes. We have also analysed how increasing the number of bagged classification trees would affect the classification accuracy. Our learning algorithm is trained with hundred and forty four samples and is tested with twenty different samples per handwriting style. In total the algorithm is trained with ten different handwriting styles. Using the proposed features and bagged classification trees, we achieve 76.4% accuracy. The practicality of the proposed method is also analysed using a few time consumption measuring parameters.