TY  - EJOU
AU  - Nahar, Khalid M. O. 
AU  - Almomani, Ammar 
AU  - Shatnawi, Nahlah 
AU  - Alauthman, Mohammad 

TI  - A Robust Model for Translating Arabic Sign Language into Spoken Arabic Using Deep Learning
T2  - Intelligent Automation \& Soft Computing

PY  - 2023
VL  - 37
IS  - 2
SN  - 2326-005X

AB  - 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.
KW  - Sign language; deep learning; transfer learning; machine learning; automatic translation of sign language; natural language processing; Arabic sign language

DO  - 10.32604/iasc.2023.038235