Large Vocabulary Arabic Online Handwriting Recognition System

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 written delayed in order. In addition, Arabic language is rich in morphology and syntax which makes it a must for a good online handwriting system to handle large vocabulary lexicon. This paper introduces a Hidden Markov Model (HMM) based system to provide solutions for most of the difficulties inherent in recognizing Arabic script. A new preprocessing technique for the delayed strokes to match the structure of the HMM model is introduced. This system use context dependent tri-Grapheme models to provide more detailed representation for the differences between the writing units. Also the used HMM models are trained with Writer Adaptive Training (WAT) to minimize the variance between writers in the training data. The models discrimination power is enhanced by a discriminative training technique which is the Minimum Grapheme Error (MGE) training. Also the Gaussian mixtures are splitted gradually to have better representation for the features space. The system results are enhanced using an additional post-processing step to rescore multiple hypothesis of the system result with higher order language Ibrahim Hosny Faculty of Computers and Information, Cairo University E-mail: [email protected] Sherif Abdou Faculty of Computers and Information, Cairo University E-mail: [email protected] Hassanin Al-Barhamtoshy Faculty of Computing and Information Technology, King Abdulaziz University E-mail: [email protected] model and cross-word HMM models. The system performance was evaluated using two different databases covering small and large lexicons. The proposed system shows a promising performance compared with stateof-art systems.