Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode

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Cite
Tubaiz, Noor, et al. “Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode”. IEEE Transactions on Human-Machine Systems, vol. 45, no. 4, 2015, pp. 526-33, https://doi.org/10.1109/thms.2015.2406692.
Tubaiz, N., Shanableh, T., & Assaleh, K. (2015). Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode. IEEE Transactions on Human-Machine Systems, 45(4), 526-533. https://doi.org/10.1109/thms.2015.2406692
Tubaiz, Noor, Tamer Shanableh, and Khaled Assaleh. “Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode”. IEEE Transactions on Human-Machine Systems 45, no. 4 (2015): 526-33. https://doi.org/10.1109/thms.2015.2406692.
Tubaiz N, Shanableh T, Assaleh K. Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode. IEEE Transactions on Human-Machine Systems. 2015;45(4):526-33.
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Refrences
Title Journal Journal Categories Citations Publication Date
Recognition of Arabic sign language alphabet using polynomial classifiers 2005
Telescopic vector composition and polar accumulated motion residuals for feature extraction in Arabic sign language recognition 2007
Automation of the Arabic sign language recognition using the Power Glove 2007
Continuous Arabic Sign Language Recognition in User Dependent Mode Journal of Intelligent Learning Systems and Applications 15 2010
10.1109/ICASSP.2007.366282
Citations
Title Journal Journal Categories Citations Publication Date
Sign Language Recognition (SLR): A Brisk Paired Deep Metric Attention Learning (BPDMAL) Model for Video Data Applications SN Computer Science 2024
Two-Stage Deep Learning Solution for Continuous Arabic Sign Language Recognition Using Word Count Prediction and Motion Images IEEE Access
  • Technology: Electrical engineering. Electronics. Nuclear engineering
  • Science: Science (General): Cybernetics: Information theory
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
2023
Integrated Mediapipe with a CNN Model for Arabic Sign Language Recognition

Journal of Electrical and Computer Engineering
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware
  • Science: Science (General): Cybernetics: Information theory
2023
Sign Language Recognition for Arabic Alphabets Using Transfer Learning Technique

Computational Intelligence and Neuroscience
  • Medicine: Medicine (General): Computer applications to medicine. Medical informatics
  • Science: Biology (General)
  • Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry
  • Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry
16 2022
Dynamic Iranian Sign Language Recognition Using an Optimized Deep Neural Network: An Implementation via a Robotic-Based Architecture International Journal of Social Robotics
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Mechanical engineering and machinery
  • Technology: Engineering (General). Civil engineering (General)
3 2021
Citations Analysis
The category Science: Science (General): Cybernetics: Information theory 2 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Design and Manufacture of Data Gloves for Rehabilitation Training and Gesture Recognition Based on Flexible Sensors and was published in 2021. The most recent citation comes from a 2024 study titled Sign Language Recognition (SLR): A Brisk Paired Deep Metric Attention Learning (BPDMAL) Model for Video Data Applications. This article reached its peak citation in 2023, with 2 citations. It has been cited in 6 different journals, 33% of which are open access. Among related journals, the SN Computer Science cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year