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dc.contributor.authorBoudjerda, Djahida-
dc.contributor.authorRehif, Fedia-
dc.date.accessioned2025-05-20T09:56:59Z-
dc.date.available2025-05-20T09:56:59Z-
dc.date.issued2023-
dc.identifier.urihttps://di.univ-blida.dz/jspui/handle/123456789/39689-
dc.description4.621.1.1368 ; 74 pfr_FR
dc.description.abstractAbstract: 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.isoenfr_FR
dc.publisherBlida1fr_FR
dc.subjectimage search, classification, CNN: Convolutional Neural Networks, Deep Learning: deep learningfr_FR
dc.titleDeep Learning pour la reconnaissance des symbols du langage des signes francaisfr_FR
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