Personal identification based on handwriting

Abstract Many techniques have been reported for handwriting-based writer identification. The majority of 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 is often 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 multi-channel Gabor filtering technique). Results of 96.0% accuracy on the classification of 1000 test documents from 40 writers are very promising. The method is shown to be robust to noise and contents.

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