Arabic Writer Identification and Verification Using Template Matching Analysis of Texture

This paper introduces an original approach for off-line, text-independent Arabic writer identification and verification. Indeed, the handwriting texture is inspected in this approach relatively to its multi-resolution aspects of directionality, angularity and curvature. The 10 resulting feature vectors are classified using common distance metrics, and then combined using a simple and experimentally proven weighting scheme. The combining of different types of textural feature vectors in our approach allowed the computer to capture efficiently the writer's individual and unique handwriting characteristics. Intensive experiments were conducted with 557 handwriting samples from 100 different writers in the IFN/ENIT database, and we obtained promising writer identification rates of 85% for Top1, 90% for Top2 and 95% for Top10. As for writer verification, an acceptable equal error rate (EER) of 5.9% ± 0.11% was obtained.

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