Recognizing handwritten Arabic script through efficient skeleton-based grapheme segmentation algorithm

To recognize unlimited set of handwritten Arabic words, an efficient segmentation algorithm is needed to segment these cursive words into a limited set of primal graphemes. We propose a rule-based segmentation algorithm that segments cursive words into graphemes through collecting special feature points from the word skeleton. The development of this algorithm is motivated by the need to solve problems and limitations available in the state-of-the-art algorithms in this area. The preliminary evaluation of the proposed algorithm is promising with over 96% accuracy on a sample subset of the IFN/ENIT database.