الفهرس 
Improving the quality of online arabic handwriting recognition
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Abstract
Online Arabic handwriting recognition 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. Previously, Hidden Markov Model (HMM) with sequence reordering have provided a successful solution for most of the difficulties inherent in recognizing Arabic handwriting. Recently, Deep Neural Networks (DNN) have shown to provide significant improvement when integrated with HMM. In this thesis we introduce the efforts done to build a large vocabulary Arabic Handwriting Recognition (HWR) system using hybrid DNN/HMM model. This system used over segmentation to provide efficient decoding. The developed system was tested using a test set of 12k words written by 100 writers with lexicon size of 125k words. The system achieved an accuracy of 71:62%, 89:61% in first recognized word and top five recognized words respectively which to our knowledge is the best reported result for large vocabulary Arabic HWR