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Towards a Lightweight Arabic Sign Language Translation System.

Mohammed Algabri1,2, Mohamed A Mekhtiche3, Mohamed A Bencherif3

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Summary
This summary is machine-generated.

This study developed a high-performance, lightweight sign language translation system for real-time communication. The system achieved high accuracy in both signer-dependent and signer-independent modes, simplifying communication for deaf and non-deaf individuals.

Keywords:
attention mechanismlightweight modelsign language translationsigner-dependent and signer-independent modes

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Effective communication between deaf and non-deaf individuals is crucial.
  • Existing sign-to-text systems often lack the performance or efficiency for real-time applications.
  • Saudi Sign Language (SSL) datasets are essential for developing specialized translation tools.

Purpose of the Study:

  • To develop a high-performance, lightweight sign-to-text translation system for real-time applications.
  • To evaluate the system's effectiveness using two Saudi Sign Language datasets (KSU-SSL and ArSL).
  • To investigate the impact of dataset size (number of signers and repetitions) on translation accuracy.

Main Methods:

  • Implementation of a sign language translation model evaluated in signer-dependent and signer-independent modes.
  • Conducting eight experiments to assess various configurations and dataset parameters.
  • Performing a comprehensive ablation study on model components, network depth, and hidden dimension size.

Main Results:

  • The system achieved 97.7% accuracy in signer-dependent mode and 90.7% in signer-independent mode on the KSU-SSL dataset.
  • On the ArSL dataset, the model reached 98.38% accuracy (signer-dependent) and 96.22% (signer-independent).
  • The study analyzed the influence of dataset characteristics and model architecture on performance.

Conclusions:

  • The developed sign-to-text system demonstrates high performance and efficiency for real-time Saudi Sign Language translation.
  • The findings highlight the importance of dataset size and signer variability in model training.
  • This research contributes to improving communication accessibility for the deaf community through advanced AI.