Online Arabic Handwriting Recognition Using Hidden Markov Models

Abstract : 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 in comparison to Latin script due to obligatory dots/stokes that are placed above or below most letters. This paper introduces a Hidden Markov Model (HMM) based system to provide solutions for most of the difficulties inherent in recognizing Arabic script including: letter connectivity, position-dependent letter shaping, and delayed strokes. This is the first HMM-based solution to online Arabic handwriting recognition. We report successful results for writerdependent and writer-independent word recognition.
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Communication dans un congrès
Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006
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Soumis le : vendredi 20 octobre 2006 - 13:22:52
Dernière modification le : samedi 17 février 2018 - 17:46:02
Document(s) archivé(s) le : mardi 6 avril 2010 - 18:24:59

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  • HAL Id : inria-00108306, version 1

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Fadi Biadsy, Jihad El-Sana, Nizar Habash. Online Arabic Handwriting Recognition Using Hidden Markov Models. Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006. 〈inria-00108306〉

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