Tolentino, L.K.S., Juan, R.O.S., Thio-ac, A.C., Pamahoy, M.A.B., Forteza, J.R.R. and Garcia, X.J.O. (2019). Static Sign Language Recognition Using Deep Learning. International Journal of Machine Learning and Computing, 9, 821-827.
https://doi.org/10.18178/ijmlc.2019.9.6.879

has been cited by the following article:

• 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.