Recognising handwritten Arabic manuscripts using a single hidden Markov model

This paper presents a new method on off-line recognition of handwritten Arabic script. The method does not require segmentation into characters, and is applied to cursive Arabic script, where ligatures, overlaps and style variation pose challenges to the recognition system. The method trains a single hidden Markov model (HMM) with the structural features extracted from the manuscript words. The HMM is composed of multiple character models where each model represents one letter from the alphabet. The performance of the proposed method is assessed using samples extracted from a historical handwirtten manuscript. 2003 Elsevier B.V. All rights reserved.