research-article

Isolated Arabic Sign Language Recognition Using a Transformer-based Model and Landmark Keypoints

Authors:

Mohammad HammoudehAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 23, Issue 1

Article No.: 3, Pages 1 - 19

https://doi.org/10.1145/3584984

Published: 15 January 2024 Publication History

Abstract

Pose-based approaches for sign language recognition provide light-weight and fast models that can be adopted in real-time applications. This article presents a framework for isolated Arabic sign language recognition using hand and face keypoints. We employed MediaPipe pose estimator for extracting the keypoints of sign gestures in the video stream. Using the extracted keypoints, three models were proposed for sign language recognition: Long-Term Short Memory, Temporal Convolution Networks, and Transformer-based models. Moreover, we investigated the importance of non-manual features for sign language recognition systems and the obtained results showed that combining hand and face keypoints boosted the recognition accuracy by around 4% compared with only hand keypoints. The proposed models were evaluated on Arabic and Argentinian sign languages. Using the KArSL-100 dataset, the proposed pose-based Transformer achieved the highest accuracy of 99.74% and 68.2% in signer-dependent and -independent modes, respectively. Additionally, the Transformer was evaluated on the LSA64 dataset and obtained an accuracy of 98.25% and 91.09% in signer-dependent and -independent modes, respectively. Consequently, the pose-based Transformer outperformed the state-of-the-art techniques on both datasets using keypoints from the signer’s hands and face.

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Akdağ ABaykan Ö(2024)Isolated sign language recognition through integrating pose data and motion history imagesPeerJ Computer Science10.7717/peerj-cs.205410(e2054)Online publication date: 21-May-2024
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Index Terms

Isolated Arabic Sign Language Recognition Using a Transformer-based Model and Landmark Keypoints
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Neural networks

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cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 23, Issue 1

January 2024

385 pages

EISSN:2375-4702

DOI:10.1145/3613498

Editor:
Imed Zitoun
Google, USA

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Publication History

Published: 15 January 2024

Online AM: 21 February 2023

Accepted: 17 February 2023

Revised: 25 December 2022

Received: 28 July 2022

Published in TALLIP Volume 23, Issue 1

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Saudi Data and AI Authority (SDAIA) and King Fahd University of Petroleum and Minerals (KFUPM) under the SDAIA-KFUPM Joint Research Center for Artificial Intelligence

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https://doi.org/10.7717/peerj-cs.2063
Akdağ ABaykan Ö(2024)Isolated sign language recognition through integrating pose data and motion history imagesPeerJ Computer Science10.7717/peerj-cs.205410(e2054)Online publication date: 21-May-2024
https://doi.org/10.7717/peerj-cs.2054
Al Ahmadi SMohammad FAl Dawsari H(2024)Efficient YOLO-Based Deep Learning Model for Arabic Sign Language RecognitionJournal of Disability Research10.57197/JDR-2024-00513:4Online publication date: 7-May-2024
https://doi.org/10.57197/JDR-2024-0051
Akdag ABaykan O(2024)Multi-Stream Isolated Sign Language Recognition Based on Finger Features Derived from Pose DataElectronics10.3390/electronics1308159113:8(1591)Online publication date: 22-Apr-2024
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Akdag ABaykan O(2024)Enhancing Signer-Independent Recognition of Isolated Sign Language through Advanced Deep Learning Techniques and Feature FusionElectronics10.3390/electronics1307118813:7(1188)Online publication date: 24-Mar-2024
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Huang JChaijaruwanich JChouvatut V(2024)Video-based Sign Language Recognition with R(2+1)D and LSTM Networks2024 16th International Conference on Knowledge and Smart Technology (KST)10.1109/KST61284.2024.10499646(214-219)Online publication date: 28-Feb-2024
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