Lexicon Reduction using Segment Descriptors for Arabic Handwriting Recognition

This paper presents a robust lexicon reduction technique using segment descriptors for Arabic handwritten text. The method segments an Arabic word into graphemes and adaptively generates a descriptor of the presence/absence of dots in those segments. The segmentation algorithm is based on the characteristic of Arabic script; which indicates predictable segmentations of Arabic characters. This in turn results in novel canonical segment descriptors for the lexicon entries. These descriptors are then used for lexicon reduction using a matching algorithm adapted for Arabic handwriting. Unlike other methods, features based on segment descriptors are computable for both word images and lexicon entries. Experimental results are reported on IfN/ENIT database which compare favorably with other approaches for lexicon reduction.