Almomani, A, M. O. Nahar, Khalid, Shatnawi, Nahlah and Alauthman, Mohammad (2023) A Robust Model for Translating Arabic Sign Language into Spoken Arabic Using Deep Learning. Intelligent Automation & Soft Computing, 37 (2). pp. 2037-2057. ISSN 1079-8587
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Abstract
This study presents a novel and innovative approach to automatically translating Arabic Sign Language (ATSL) into spoken Arabic. The proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition models. The image-based translation method maps sign language gestures to corresponding letters or words using distance measures and classification as a machine learning technique. The results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs, with a translation accuracy of 93.7%. This research makes a significant contribution to the field of ATSL. It offers a practical solution for improving communication for individuals with special needs, such as the deaf and mute community. This work demonstrates the potential of deep learning techniques in translating sign language into natural language and highlights the importance of ATSL in facilitating communication for individuals with disabilities.
Affiliation: | Skyline University College |
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SUC Author(s): | Almomani, A ORCID: https://orcid.org/0000-0002-8808-6114 |
All Author(s): | Almomani, A, M. O. Nahar, Khalid, Shatnawi, Nahlah and Alauthman, Mohammad |
Item Type: | Article |
Uncontrolled Keywords: | Sign language; deep learning; transfer learning; machine learning; automatic translation of sign language; natural language processing; Arabic sign language |
Subjects: | B Information Technology > BL Machine Learning B Information Technology > BR Deep Learning |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Mosys Team |
Date Deposited: | 25 Dec 2023 13:40 |
Last Modified: | 25 Dec 2023 13:40 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/727 |
Publisher URL: | https://doi.org/10.32604/iasc.2023.038235 |
Publisher OA policy: | https://v2.sherpa.ac.uk/id/publication/24946?templ... |
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