Université Blida 1

Deep Learning pour la reconnaissance des symbols du langage des signes francais

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dc.contributor.author Boudjerda, Djahida
dc.contributor.author Rehif, Fedia
dc.date.accessioned 2025-05-20T09:56:59Z
dc.date.available 2025-05-20T09:56:59Z
dc.date.issued 2023
dc.identifier.uri https://di.univ-blida.dz/jspui/handle/123456789/39689
dc.description 4.621.1.1368 ; 74 p fr_FR
dc.description.abstract Abstract: Hand gestures play a very important role in our daily life as one of the richest means of non-verbal communication. Thus, in the field of Human-Computer Interaction (HMI). In our thesis, we used deep learning and convolutional neural networks (CNNs) to classify a database of images into a set of classes (images of American Sign Language alphabets), we used Matlab, we are going to present through this memory we presented our application by giving some screenshots which explain the progress and the functioning of our work, as well as the results obtained. fr_FR
dc.language.iso en fr_FR
dc.publisher Blida1 fr_FR
dc.subject image search, classification, CNN: Convolutional Neural Networks, Deep Learning: deep learning fr_FR
dc.title Deep Learning pour la reconnaissance des symbols du langage des signes francais fr_FR


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