Efficient Arabic Handwritten Character Recognition based on Machine Learning and Deep Learning Approaches

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dc.contributor.authorSaid Gadri
dc.date.accessioned2021-05-31T08:34:06Z
dc.date.available2021-05-31T08:34:06Z
dc.date.issued2020
dc.description.abstractArabic Handwritten character recognition is one of the most studied topics since many decades, there exists many difficulties which prevent to have significant advances in this important field such as: the variability of handwriting from a person to another, the large availability of databases, the complicated morphology of Arabic as a very rich Semitic language. In this paper, we proposed a deep learning model based on convolutional neural networks CNN which permits to achieve a high performance in Arabic handwritten characters recognition.en_US
dc.identifier.urihttps://dspace.univ-msila.dz/handle/123456789/24331
dc.publisherUniversité de M'silaen_US
dc.subjectAI, Machine Learning, Deep Learning, Arabic Character Recognition, Convolutional Neural Networks.en_US
dc.titleEfficient Arabic Handwritten Character Recognition based on Machine Learning and Deep Learning Approachesen_US
dc.typeArticleen_US

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