A Robust Model for Translating Arabic Sign Language into Spoken Arabic Using Deep Learning  

作　　者：Khalid M.O.Nahar Ammar Almomani Nahlah Shatnawi Mohammad Alauthman 

机构地区：[1]Department of Computer Sciences,Faculty of Information Technology and Computer Sciences,Yarmouk University-Irbid,21163,Jordan [2]School of Computing,Skyline University College,Sharjah,P.O.Box 1797,United Arab Emirates [3]IT-Department-Al-Huson University College,Al-Balqa Applied University,P.O.Box 50,Irbid,Jordan [4]Department of Information Security,Faculty of Information Technology,University of Petra,Amman,Jordan

出　　处：《Intelligent Automation & Soft Computing》2023年第8期2037-2057,共21页智能自动化与软计算（英文）

摘　　要：This study presents a novel and innovative approach to auto-matically 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 corre-sponding 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.

关 键 词：Sign language deep learning transfer learning machine learning automatic translation of sign language natural language processing Arabic sign language 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]