Using codebooks generated from text skeletonization for forensic writer identification

In this paper, we propose a novel approach for writer identification using codebook generation based on text skeletonization.Unlike other schemes, the skeleton in this approach is segmented at its junction pixels into elementary graphic units called graphemes. The codebook is generated by clustering the graphemes according to their distributions into a predefined grid. This method has been evaluated using the benchmarking dataset of the International Conference on Document Analysis and Recognition (ICDAR 2011) writer identification contest and has shown promising results. We also studied the effect of the amount of handwriting on the identification accuracy of the method and demonstrated that the proposed method is valid for Latin and Greek languages.