Effective Recognition of Handwritten Arabic Text

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dc.contributor.authorZennaki, Mahmoud
dc.contributor.authorSadouni, Kaddour
dc.contributor.authorMamouni, El Mamoun
dc.date.accessioned2022-04-12T12:33:00Z
dc.date.available2022-04-12T12:33:00Z
dc.date.issued2022-01-24
dc.descriptionForum Intervention of Artificial Intelligence and Its Applications. Faculty of Exat science. University of Eloueden_US
dc.description.abstractThis paper presents an effective method for segmenting handwritten Arabic words based on projection histograms, as well as a comprehensive study of overlap letters, which represent a major problem in the recognition of Arabic script. The characters resulting from segmentation are recognized by an SVM classifier that was trained on a corpus of handwritten Arabic characters that also includes the main overlap letters identified in Arabic script. The proposed method was tested using the IFN/ENIT database and encouraging results were obtained.en_US
dc.identifier.citationZennaki, Mahmoud • Sadouni, Kaddour • Mamouni, El Mamoun. Effective Recognition of Handwritten Arabic Text. Forum of Artificial Intelligence and Its Applications. 24-26 Jan 2022. Faculty of Exat science. University of Eloued. [visited in ../../….]. available from [copy the link here]en_US
dc.identifier.urihttp://dspace.univ-eloued.dz/handle/123456789/10795
dc.language.isoenen_US
dc.publisherUniversity of Eloued جامعة الواديen_US
dc.subjectArabic text · handwritten · segmentation · projection histogram · support vector machinesen_US
dc.titleEffective Recognition of Handwritten Arabic Texten_US
dc.typeOtheren_US

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