Machine Learning and Deep Learning Approaches for Arabic Sign Language Recognition: A Decade Systematic Literature Review

Sign language (SL) is a means of communication that is used to bridge the gap between the deaf, hearing-impaired, and others. For Arabic speakers who are hard of hearing or deaf, Arabic Sign Language (ArSL) is a form of nonverbal communication. The development of effective Arabic sign language recognition (ArSLR) tools helps facilitate this communication, especially for people who are not familiar with ArSLR. Although researchers have investigated various machine learning (ML) and deep learning (DL) methods and techniques that affect the performance of ArSLR systems, a systematic review of these methods is lacking. The objectives of this study are to present a comprehensive overview of research on ArSL recognition and present insights from previous research papers. In this study, a systematic literature review of ArSLR based on ML/DL methods and techniques published between 2014 and 2023 is conducted. Three online databases are used: Web of Science (WoS), IEEE Xplore, and Scopus. Each study has undergone the proper screening processes, which include inclusion and exclusion criteria. Throughout this systematic review, PRISMA guidelines have been appropriately followed and applied. The results of this screening are divided into two parts: analysis of all the datasets utilized in the reviewed papers, underscoring their characteristics and importance, and discussion of the ML/DL techniques' potential and limitations. From the 56 articles included in this study, it was noticed that most of the research papers focus on fingerspelling and isolated word recognition rather than continuous sentence recognition, and the vast majority of them are vision-based approaches. The challenges remaining in the field and future research directions in this area of study are also discussed.