JIHAD EL SANA

In this paper, we present a multi-level recognizer for online Arabic handwriting. In Arabic script (handwritten and printed), cursive writing - is not a style - it is an inherent part of the script. In addition, the connection between letters is done with almost no ligatures, which complicates segmenting a word into individual letters. In this work, we have adopted the holistic approach and avoided segmenting words into individual letters. To reduce the search space, we apply a series of filters in a hierarchicalmanner. The earlier filters perform light processing on a large number of candidates, and the later filters perform heavy processing on a small number of candidates. In the first filter, global features and delayed strokes patterns are used to reduce candidate word-part models. In the second filter, local features are used to guide a dynamic time warping (DTW) classification. The resulting k top ranked candidates are sent for shape-context based classifier, which determines the recognized word-part. In this work, we have modified the classic DTW to enable different costs for the different operations and control their behavior. We have performed several experimental tests and have received encouraging results.