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dc.contributor.authorBernard, Anne-Laure Bianneen_US
dc.contributor.authorMenasri, Faresen_US
dc.contributor.authorHajj Mohamad, Ramy Alen_US
dc.contributor.authorMokbel, Chaficen_US
dc.contributor.authorKermorvant, Christopheren_US
dc.contributor.authorLikforman-Sulem, Laurenceen_US
dc.description.abstractThis study aims at building an efficient word recognition system resulting from the combination of three handwriting recognizers. The main component of this combined system is an HMM-based recognizer which considers dynamic and contextual information for a better modeling of writing units. For modeling the contextual units, a state-tying process based on decision tree clustering is introduced. Decision trees are built according to a set of expert-based questions on how characters are written. Questions are divided into global questions, yielding larger clusters, and precise questions, yielding smaller ones. Such clustering enables us to reduce the total number of models and Gaussians densities by 10. We then apply this modeling to the recognition of handwritten words. Experiments are conducted on three publicly available databases based on Latin or Arabic languages: Rimes, IAM, and OpenHart. The results obtained show that contextual information embedded with dynamic modeling significantly improves recognition.en_US
dc.format.extent14 p.en_US
dc.subjectHidden Markov modelsen_US
dc.subjectContext modelingen_US
dc.subjectFeature extractionen_US
dc.subjectHandwriting recognitionen_US
dc.subjectComputational modelingen_US
dc.titleDynamic and Contextual Information in HMM Modeling for Handwritten Word Recognitionen_US
dc.typeJournal Articleen_US
dc.contributor.affiliationDepartment of Electrical Engineeringen_US
dc.relation.ispartoftextIEEE transactions on pattern analysis and machine intelligenceen_US
dc.provenance.recordsourceOliben_US of Engineering-
Appears in Collections:Department of Electrical Engineering
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