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dc.contributor.authorChammas, Edgaren_US
dc.contributor.authorMokbel, Chaficen_US
dc.contributor.authorLikforman-Sulem, Laurenceen_US
dc.description.abstractArabic handwritten documents present specific challenges due to the cursive nature of the writing and the presence of diacritical marks. Moreover, one of the largest labeled database of Arabic handwritten documents, the OpenHart-NIST database includes specific noise, namely guidelines, that has to be addressed. We propose several approaches to process these documents. First a guideline detection approach has been developed, based on K-means, that detects the documents that include guidelines. We then propose a series of preprocessing at text-line level to reduce the noise effects. For text-lines including guidelines, a guideline removal preprocessing is described and existing keystroke restoration approaches are assessed. In addition, we propose a preprocessing that combines noise removal and deskewing by removing line fragments from neighboring text lines, while searching for the principal orientation of the text-line. We provide recognition results, showing the significant improvement brought by the proposed processings.en_US
dc.format.extent5 p.en_US
dc.subjectHidden Markov modelsen_US
dc.subjectImage recognitionen_US
dc.subjectOptical imagingen_US
dc.subjectOptical reflectionen_US
dc.subjectText recognitionen_US
dc.subject.lcshImage segmentationen_US
dc.titleArabic handwritten document preprocessing and recognitionen_US
dc.typeConference Paperen_US
dc.relation.conferenceInternational Conference on Document Analysis and Recognition (ICDAR) (13th : 23-26 Aug 2015 : Tunisia)en_US
dc.contributor.affiliationDepartment of Electrical Engineeringen_US
dc.relation.ispartoftext2015 13th International Conference on Document Analysis and Recognition (ICDAR)en_US
Appears in Collections:Department of Electrical Engineering
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