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DC Field | Value | Language |
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dc.contributor.author | Afafe. Lahreche, Abdelhakim. Cheriet | - |
dc.date.accessioned | 2019-11-11T07:46:18Z | - |
dc.date.available | 2019-11-11T07:46:18Z | - |
dc.date.issued | 2019-11-11 | - |
dc.identifier.uri | http://dspace.univ-ouargla.dz/jspui/handle/123456789/21928 | - |
dc.description.abstract | The main objectives of our work is using the e ectiveness of the Deep learning methods for Arabic manuscript recognition. In this work, we used Convolution Neural Networks where we train and test them using Tensor ow library that support machine learning in Python in order to obtain the required results. | en_US |
dc.description.sponsorship | UNIVERSITY KASDI-MERBAH OUARGLA Faculty of New Technologies of Information and Communication Department of Computer Science and Information Technologies | en_US |
dc.language.iso | en | en_US |
dc.subject | Handwriting, deep learning, Neural Networks, Convolution Neural Net- works. R esum e Les principaux objectifs de notre travail est l'utilisation de l' e cacit e du Deep learning m ethodes pour la reconnaissance des manuscrits en arabe. Dans ce travail, jai utilis e des r eseau neuronal convolutif o u nous les avons form es et test es a laide de biblioth eques Tensor ow qui prend en charge lapprentissage automatique en Python a n dobtenir les r esultats requis. Mots-cl es: ecriture manuscrite, apprentissage en profondeur, r eseaux de neurones, r eseaux de neurones de convolution. 1 | en_US |
dc.title | Deep learning for Arabic letters recognition | en_US |
Appears in Collections: | Département d'Electronique et des Télécommunications - Master |
Files in This Item:
File | Description | Size | Format | |
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Deep learning for Arabic letters recognition.pdf | 454,74 kB | Adobe PDF | View/Open |
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