Jove
Visualize
Contact Us

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Real-Time Air-Writing Recognition for Arabic Letters Using Deep Learning.

Sensors (Basel, Switzerland)ยท2024
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 4, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

949

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

Asmaa Alayed1

  • 1Department of Software Engineering, College of Computing, Umm Al-Qura University, Makkah 21955, Saudi Arabia.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
Summary

This systematic review analyzes machine learning and deep learning for Arabic Sign Language Recognition (ArSLR). Most research focuses on isolated words using vision-based methods, highlighting a need for continuous ArSLR development.

Keywords:
Arabic sign language (ArSL)Arabic sign language recognition (ArSLR)datasetdeep learninghand gesture recognitionmachine learning

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.3K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K

Related Experiment Videos

Last Updated: Jun 4, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

949
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.3K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Arabic Sign Language (ArSL) is crucial for communication among Arabic speakers who are deaf or hearing-impaired.
  • Effective Arabic Sign Language Recognition (ArSLR) tools are needed to bridge communication gaps, especially for non-signers.
  • A systematic review of machine learning (ML) and deep learning (DL) methods for ArSLR is currently lacking.

Purpose of the Study:

  • To provide a comprehensive overview of ArSL recognition research.
  • To analyze ML/DL methods and techniques used in ArSLR systems.
  • To identify challenges and future research directions in ArSLR.

Main Methods:

  • A systematic literature review of ArSLR research published between 2014 and 2023.
  • Searches conducted across three major databases: Web of Science (WoS), IEEE Xplore, and Scopus.
  • Adherence to PRISMA guidelines for study screening, inclusion, and exclusion criteria.

Main Results:

  • Analysis of 56 included articles, focusing on datasets and ML/DL techniques.
  • Predominance of vision-based approaches in ArSLR research.
  • Most studies concentrate on fingerspelling and isolated word recognition, with limited work on continuous sentence recognition.

Conclusions:

  • Current ArSLR research is heavily skewed towards isolated sign recognition using visual data.
  • Significant challenges remain in developing systems for continuous ArSL recognition.
  • Future research should prioritize continuous ArSL recognition and explore diverse methodologies beyond vision-based approaches.