Strategies for Large Handwritten Farsi/Arabic Lexicon Reduction

Given large number of words to be recognized, lexicon reduction strategy for eliminating unlikely candidates before recognition can be a reasonable and powerful approach for increasing the recognition speed. In this paper, we describe a holistic approach for large Arabic handwritten lexicon reduction which is based on inherent properties of Arabic writing. The principal of this technique involves extraction of dots, diacritics and subwords from the cursive Arabic word image to describe its shape. In the first stage of lexicon reduction, the number of subwords in the input word is estimated. Then, in the second stage, the word descriptor, based on the dots and diacritics information, is used while taking into account only the candidates selected in the first stage. Experimental results on IFN/ENIT database, consisting of 26,459 cursive Arabic word images, show a lexicon reduction of 92.5% with accuracy of 74%.