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Related Experiment Video

Updated: Jan 11, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Self-supervised learning with a contrastive VideoMoCo framework for Saudi Arabic sign language recognition using 3D

Mahmoud Rokaya1, Dalia I Hemdan2, Mohammed A Alzain3

  • 1Department of Information Technology, College of Computers and Information Technology, Taif University, 21944, Taif, Saudi Arabia. mahmoudrokaya@tu.edu.sa.

Scientific Reports
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a self-supervised learning framework for Saudi Arabic Sign Language (SArSL) recognition, achieving 92.7% F1-score. The approach enhances accessibility for the Saudi Deaf community through improved gesture recognition.

Keywords:
3D convolutional neural networksArabic sign language recognitionContrastive learningSaudi arabic sign language (ArSL)Self-Supervised learningVideoMoCo

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Saudi Arabic Sign Language (SArSL) recognition is challenging due to complex spatio-temporal dynamics and limited annotated data.
  • Existing methods struggle with the intricacies of SArSL, hindering effective communication for the Saudi Deaf community.

Purpose of the Study:

  • To develop a robust and scalable self-supervised learning framework for accurate Saudi Arabic Sign Language recognition.
  • To improve the performance and accessibility of SArSL recognition systems.

Main Methods:

  • A self-supervised learning framework using Video Momentum Contrast (VideoMoCo) with a 3D ResNet-50 backbone was developed.
  • The model was pre-trained on 18,000 unlabeled gesture videos and fine-tuned on the KARSL-502 dataset (15,400 samples, 502 classes).

Main Results:

  • The proposed framework achieved a 92.7% F1-score, significantly outperforming baseline models (CNN-LSTM: 86.0%, Two-Stream CNN: 84.5%).
  • Demonstrated robustness to class imbalance, motion variations, and visual noise, with a low inference latency of 12 ms per batch.
  • Ablation studies confirmed the effectiveness of the momentum encoder and negative sample queue for feature learning.

Conclusions:

  • The VideoMoCo-ResNet-50 framework provides a scalable and inclusive foundation for real-time SArSL recognition.
  • This advancement enhances accessibility for the Saudi Deaf community and supports future multimodal applications.