Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/97
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dc.contributor.authorLikforman-Sulem, Laurenceen_US
dc.contributor.authorHajj Mohamad, Ramy Alen_US
dc.contributor.authorMokbel, Chaficen_US
dc.contributor.authorMenasri, Faresen_US
dc.contributor.authorBernard, Anne-Laure Bianneen_US
dc.contributor.authorKermorvant, Christopheren_US
dc.date.accessioned2020-12-23T08:25:41Z-
dc.date.available2020-12-23T08:25:41Z-
dc.date.issued2012-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/97-
dc.description.abstractHMM-based systems need observation sequences as input. These observations consist of discrete values or vectors extracted from word images or text lines. In this chapter we explore various types of features which are popular for Arabic cursive handwriting recognition. Some of these features are statistical, based on pixel distributions or local directions. Others are structural, based on the presence of loops, ascenders, or descenders. We show how these features can be efficient within HMM-based systems based on sliding windows or grapheme segmentation.en_US
dc.format.extent20 p.en_US
dc.language.isoengen_US
dc.titleFeatures for HMM-Based Arabic Handwritten Word Recognition Systemsen_US
dc.typeBook Chapteren_US
dc.contributor.affiliationDepartment of Electrical Engineeringen_US
dc.description.startpage123en_US
dc.description.endpage143en_US
dc.date.catalogued2019-05-24-
dc.description.statusPublisheden_US
dc.identifier.OlibID192050-
dc.relation.ispartoftextV. Märgner & H. El Abed (Eds.), Guide to OCR for Arabic Scripts. Springer.en_US
dc.provenance.recordsourceOliben_US
crisitem.author.parentorgFaculty of Engineering-
Appears in Collections:Department of Electrical Engineering
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