In this paper, we evaluate the performance on Arabic handwriting of the text-independent writer identification methods that we developed and tested on Western script in recent years. We use the IFN/ENIT data in the experiments reported here and our tests involve 350 writers. The results show that our methods are very effective and the conclu- sions drawn in previous studies remain valid also on Arabic script. High performance is achieved by combining textu- ral features (joint directional probability distributions) with allographic features (grapheme-emission distributions).
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