Personal identification based on handwriting
Many techniques have been reported for handwriting-based writer identification. Most techniques assume that the written text is fixed (e.g., in signature verification). In this paper we attempt to eliminate this assumption by presenting a novel algorithm for automatic text-independent writer identification. Given that the handwriting of different people can often be visually distinctive, we take a global approach based on texture analysis, where each writer's handwriting is regarded as a different texture. In principle this allows us to apply any standard texture recognition algorithm for the task (e.g., the multichannel Gabor filtering technique). Results of 95.0% accuracy on the classification of 300 test documents front 20 writers are very promising. The method is shown to be robust to noise and contents.