Arabic Sign Language Datasets: Review and Improvements

Tamimi, Alaa Jalal; Hashlamon, Iyad

doi:10.1109/isncc58260.2023.10323881

2023 International Symposium on Networks, Computers and Communications (ISNCC) 2023

DOI: 10.1109/isncc58260.2023.10323881

|View full text |Cite

Arabic Sign Language Datasets: Review and Improvements

Alaa Jalal Tamimi,

Iyad Hashlamon

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

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

Alayed

2024

Sensors

View full text Add to dashboard Cite

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.

show abstract

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

Alayed

2024

Sensors

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Arabic Sign Language Datasets: Review and Improvements

Cited by 1 publication

References 12 publications

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

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

Contact Info

Product

Resources

About