%0 Journal Article
%T Deep Learning Recognition for Arabic Alphabet Sign Language RGB Dataset
%A Rabie El Kharoua
%A Xiaoming Jiang
%J Journal of Computer and Communications
%P 32-51
%@ 2327-5227
%D 2024
%I Scientific Research Publishing
%R 10.4236/jcc.2024.123003
%X This paper introduces a Convolutional Neural Network (CNN) model for Arabic Sign Language (AASL) recognition, using the AASL dataset. Recognizing the fundamental importance of communication for the hearing-impaired, especially within the Arabic-speaking deaf community, the study emphasizes the critical role of sign language recognition systems. The proposed methodology achieves outstanding accuracy, with the CNN model reaching 99.9% accuracy on the training set and a validation accuracy of 97.4%. This study not only establishes a high-accuracy AASL recognition model but also provides insights into effective dropout strategies. The achieved high accuracy rates position the proposed model as a significant advancement in the field, holding promise for improved communication accessibility for the Arabic-speaking deaf community.
%K Convolutional Neural Network (CNN)
%K AASL Dataset
%K Dropout
%K Deep Learning
%K Communication Technology
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=131670