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Conference papers

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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https://hal.inria.fr/inria-00108306
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Submitted on : Friday, October 20, 2006 - 1:22:52 PM
Last modification on : Friday, December 18, 2020 - 6:46:04 PM
Long-term archiving on: : Tuesday, April 6, 2010 - 6:24:59 PM

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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. Tenth International Workshop on Frontiers in Handwriting Recognition, Université de Rennes 1, Oct 2006, La Baule (France). ⟨inria-00108306⟩

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