Online arabic handwriting recognition using hidden markov models

Fadi Biadsy, Jihad El-Sana, Nizar Y Habash

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 writer-dependent and writer-independent word recognition.
Original languageEnglish GB
Title of host publicationProceedings of the 10th International Workshop on Frontiers of Handwriting and Recognition
StatePublished - 2006


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