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Glorot, X., et al. (2010) Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th International Conference on International Conference on Machine Learning, Haifa Israel, 21-24 June 2010, 807-814.

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• TITLE: Deep Learning Recognition for Arabic Alphabet Sign Language RGB Dataset

  AUTHORS: Rabie El Kharoua, Xiaoming Jiang

  KEYWORDS: Convolutional Neural Network (CNN), AASL Dataset, Dropout, Deep Learning, Communication Technology

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.3, March 11, 2024

  ABSTRACT: 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.