1
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
(aaalhussan@pnu.edu.sa)
2
Faculty of Artiļ¬cial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt
(mmm@ieee.org)
3
Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia
(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.
References :
[1] Aldhahri E, Aljuhani R, Alfaidi A, Alshehri B, Alwadei H, Aljojo N, Almazroi A, Arabic sign language recognition using convolutional neural network and mobilenet. Arabian Journal for Science and Engineering, 48(2), 2147-2154, 2023.
[2] Mustafa M, A study on Arabic sign language recognition for differently abled using advanced machine learning classifiers. Journal of Ambient Intelligence and Humanized Computing, 12, 4101-4115, 2021.
[3] Kamruzzaman M M, Arabic sign language recognition and generating Arabic speech using convolutional neural network. Wireless Communications and Mobile Computing, 2020.
[4] M. Saber, Efficient phase recovery system, Indonesian Journal of Electrical Engineering and Computer Science (lJEECS), 5(1), 123-129, 2017.
[5] Alani A A, Cosma G, ArSL-CNN: a convolutional neural network for Arabic sign language gesture recognition. Indonesian journal of electrical engineering and computer science, 22, 2021.
[6] M Saber, Y Jitsumatsu, MTA Khan, A simple design to mitigate problems of conventional digital phase locked loop, Signal Processing: An international journal (SPIJ), 6(2), 65-77, 2012.
[7] El-kenawy El-Sayed Towfek, Ali Ibraheem El-Desoky, Amany M. Sarhan, A bidder behavior learning intelligent system for trust measurement. International Journal of Computer Applications, 975, 2014.
[8] Alharbi AH et al., Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm. Biomimetics, 8(3),313, 2023.
[9] AlKhuraym B Y, Ismail M M B, Bchir O, Arabic Sign Language Recognition Using Lightweight CNN-Based Architecture. International Journal of Advanced Computer Science and Applications, 13(4), 2022.
[10] Ismail M H, Dawwd S A, Ali F H, Dynamic hand gesture recognition of Arabic sign language by using deep convolutional neural networks. Indones. J. Electr. Eng. Comput. Sci, 25, 952-962, 2022.
[11] Mohamed Saber, A novel design and Implementation of FBMC transceiver for low power applications, Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 8(1), 83-93, 2020.
[12] Latif G, Mohammad N, Alghazo J, AlKhalaf R, AlKhalaf R, ArASL: Arabic alphabets sign language dataset. Data in brief, 23, 103777, 2019.
[13] Amin Samy, Sayed A. Ward, Mahmud N Ali, Conventional Ratio and Artificial Intelligence (AI) Diagnostic methods for DGA in Electrical Transformers. International Electruical Engineering Journal, 6, 2096-2102, 2015.
[14] Mohamed A. Abouelatta, et al. , Measurement and assessment of corona current density for HVDC bundle conductors by FDM integrated with full multigrid technique. Electric Power Systems Research, 199, 2021.
[15] E. M. Shaalan, S. M. Ghania and S. A. Ward, Analysis of electric field inside HV substations using charge simulation method in three dimensional. Annual Report Conference on Electrical Insulation and Dielectic Phenomena, West Lafayette, IN, USA,1-5, 2010.
[16] M. M. E. Bahy, S. A. Ward, M. Badawi and R. Morsi, "Particle-initiated negative corona in co-axial cylindrical configuration. Annual Report Conference on Electrical Insulation and Dielectric Phenomena, Montreal, QC, Canada, 343-348, 2012.
[17] Eid Marwa M, Fawaz Alassery, Abdelhameed Ibrahim, and Mohamed Saber, Metaheuristic optimization algorithm for signals classification of electroencephalography channels. Computers, Materials & Continua, 71(3), 4627-4641, 2022.
| Style | # |
|---|---|
| MLA | Amel Ali Alhussan, Marwa M. Eid, Wei Hong Lim. "Advancing Communication for the Deaf: A Convolutional Model for Arabic Sign Language Recognition." Journal of Artificial Intelligence and Metaheuristics, Vol. 5, No. 1, 2023 ,PP. 38-45 (Doi : https://doi.org/10.54216/JAIM.050104) |
| APA | Amel Ali Alhussan, Marwa M. Eid, Wei Hong Lim. (2023). Advancing Communication for the Deaf: A Convolutional Model for Arabic Sign Language Recognition. Journal of Journal of Artificial Intelligence and Metaheuristics, 5 ( 1 ), 38-45 (Doi : https://doi.org/10.54216/JAIM.050104) |
| Chicago | Amel Ali Alhussan, Marwa M. Eid, Wei Hong Lim. "Advancing Communication for the Deaf: A Convolutional Model for Arabic Sign Language Recognition." Journal of Journal of Artificial Intelligence and Metaheuristics, 5 no. 1 (2023): 38-45 (Doi : https://doi.org/10.54216/JAIM.050104) |
| Harvard | Amel Ali Alhussan, Marwa M. Eid, Wei Hong Lim. (2023). Advancing Communication for the Deaf: A Convolutional Model for Arabic Sign Language Recognition. Journal of Journal of Artificial Intelligence and Metaheuristics, 5 ( 1 ), 38-45 (Doi : https://doi.org/10.54216/JAIM.050104) |
| Vancouver | Amel Ali Alhussan, Marwa M. Eid, Wei Hong Lim. Advancing Communication for the Deaf: A Convolutional Model for Arabic Sign Language Recognition. Journal of Journal of Artificial Intelligence and Metaheuristics, (2023); 5 ( 1 ): 38-45 (Doi : https://doi.org/10.54216/JAIM.050104) |
| IEEE | Amel Ali Alhussan, Marwa M. Eid, Wei Hong Lim, Advancing Communication for the Deaf: A Convolutional Model for Arabic Sign Language Recognition, Journal of Journal of Artificial Intelligence and Metaheuristics, Vol. 5 , No. 1 , (2023) : 38-45 (Doi : https://doi.org/10.54216/JAIM.050104) |