Using advanced Hidden Markov Models for online Arabic handwriting recognition

Online handwriting recognition of Arabic script is a difficult problem since it is naturally both cursive and unconstrained. The analysis of Arabic script is further complicated due to obligatory dots/stokes that are placed above or below most letters and usually are written delayed in order. This paper introduces a Hidden Markov Model (HMM) based system to provide solutions for most of the difficulties inherent in recognizing Arabic script. A preprocessing for the delayed strokes to match the structure of the HMM model is introduced. The used HMM models are trained with Writer Adaptive Training (WAT) to minimize the variance between writers in the training data. Also the models discrimination power is enhanced with Discriminative training. The system performance is evaluated using an international test set from the ADAB completion and shows a promising performance compared with the state-of-art systems.