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.