Recognition of arabic handwritten letters using deep Learning approach

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dc.contributor.authorChiekhaoui, Marouane
dc.contributor.authorAichouche, Ali
dc.contributor.authorSupervisor: Bentrcia, Rahima
dc.date.accessioned2023-11-14T13:51:26Z
dc.date.available2023-11-14T13:51:26Z
dc.date.issued2023-06-10
dc.description.abstractMany languages have made significant advancements in the field of character recognition, including English, Chinese, Japanese, and French, with recognition rates reaching up to 100% in some cases. However, Arabic handwriting recognition faces lower recognition rates, primarily due to certain linguistic characteristics that make the recognition process more challenging, along with a shortage of high-quality available datasets. Therefore, this memorandum was undertaken with the aim of developing a system for recognizing handwritten Arabic characters and a word segmentation system. The study began with an analysis of the Arabic language's structure, followed by an overview of deep neural network technology, which has proven its efficiency in achieving rapid and reliable recognition results. Finally, the obtained results were explained and interpreted.en_US
dc.identifier.urihttp://dspace.univ-msila.dz:8080//xmlui/handle/123456789/41277
dc.language.isoenen_US
dc.publisherUniversity of M'silaen_US
dc.subjectrecognition of handwritten Arabic letters; segmentation of handwritten Arabic words; convolutional neural networks; processing; Feature extraction; classification;en_US
dc.titleRecognition of arabic handwritten letters using deep Learning approachen_US
dc.typeThesisen_US

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