Advancing Communication for the Deaf: A Convolutional Model for Arabic Sign Language Recognition

Amel Ali Alhussan^*1, Marwa M. Eid², Wei Hong Lim³

¹ Department of Computer Sciences, College of Computer and Information Sciences,

Princess Nourah bint Abdulrahman University, P.O. Box 84428,

 Riyadh 11671, Saudi Arabia

² Faculty of Artificial Intelligence, Delta University for Science and Technology,

 Mansoura 11152, Egypt

³ Faculty of Engineering, Technology and Built Environment, UCSI University,

 Kuala Lumpur 56000, Malaysia

Emails: aaalhussan@pnu.edu.sa; mmm@ieee.org; limwh@ucsiuniverisity.edu.my

Abstract

For the deaf population that speaks Arabic, Arabic Sign Language (ArSL) is an essential means of communication. This research presents a convolutional model for recognizing Arabic sign language because of the importance of clear communication. We hope to improve the deaf community's access to communication and broaden its sense of belonging by harnessing deep learning's power and fine-tuning the model to ArSL's particularities. To represent the complex hand movements and visual patterns that are characteristic of ArSL, the proposed model makes use of a variety of carefully made architectural decisions, such as the number of layers, the size of the kernels, the activation functions, and the pooling approaches. Our model outperforms state-of-the-art machine learning techniques, as shown by experimental findings on a large dataset. These results not only lay the groundwork for future developments in sign language recognition, but also demonstrate the promise of our technique in improving communication for the Arabic-speaking deaf community.

Keywords: Arabic Sign Language; sign language recognition; convolutional model; deep learning; communication accessibility; inclusivity.