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An Intelligent Real-Time System for Sentence-Level Recognition of Continuous Saudi Sign Language Using Landmark-Based

Adel BenAbdennour1, Mohammed Mukhtar1, Osama Almolike1

  • 1Department of Electrical Engineering, Faculty of Engineering, Islamic University of Madinah, Madinah 42351, Saudi Arabia.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary

This study introduces a real-time Saudi Sign Language (SSL) recognition system that translates continuous signs into spoken Arabic. The system achieves 94.2% accuracy, bridging communication gaps for Deaf and Hard-of-Hearing individuals.

Keywords:
Bidirectional Long Short-Term MemorySaudi Sign Languageartificial intelligenceassistive technologiesdeep learningdisabilitylarge language modelsentence-level recognitionsign languagetemporal modeling

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Communication barriers persist between sign language users and the hearing community, especially in regions lacking automated translation.
  • Saudi Arabia faces amplified communication challenges due to reliance on Saudi Sign Language (SSL) and a scarcity of real-time translation systems.

Purpose of the Study:

  • To develop and evaluate a real-time, end-to-end system for continuous Saudi Sign Language (SSL) sentence recognition.
  • To directly map recognized SSL sentences to natural spoken Arabic output.
  • To address the communication gap for Deaf and Hard-of-Hearing individuals in Saudi Arabia.

Main Methods:

  • Utilized MediaPipe Holistic for spatio-temporal landmark feature extraction from video streams.
  • Employed a Bidirectional Long Short-Term Memory (BiLSTM) network trained on the ArabSign (ArSL) dataset for sentence-level classification.
  • Incorporated an idle-based segmentation mechanism for natural, uninterrupted signing and evaluated using Leave-One-Signer-Out (LOSO) cross-validation.

Main Results:

  • Achieved a mean sentence-level accuracy of 94.2% using the LOSO protocol, outperforming the baseline (92.07%).
  • Demonstrated robust generalization and real-time performance suitable for interactive applications.
  • An optional LLM-based refinement stage was integrated for enhanced linguistic fluency in the Arabic output.

Conclusions:

  • Direct sentence-level modeling, combined with landmark-based features and real-time segmentation, offers an effective solution for continuous SSL recognition.
  • The developed system significantly improves real-time communication accessibility for SSL users.
  • The approach provides a practical foundation for developing advanced sign language translation technologies.