Personal identification based on handwriting

Many techniques have been reported for handwriting-based writer identi"cation. The majority of techniques assume that the written text is "xed (e.g., in signature veri"cation). In this paper we attempt to eliminate this assumption by presenting a novel algorithm for automatic text-independent writer identi"cation. Given that the handwriting of di!erent people is often visually distinctive, we take a global approach based on texture analysis, where each writer's handwriting is regarded as a di!erent texture. In principle, this allows us to apply any standard texture recognition algorithm for the task (e.g., the multi-channel Gabor "ltering technique). Results of 96.0% accuracy on the classi"cation of 1000 test documents from 40 writers are very promising. The method is shown to be robust to noise and contents. ( 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.