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Efhamni: A Deep Learning-Based Saudi Sign Language Recognition Application.

Lama Al Khuzayem1, Suha Shafi1, Safia Aljahdali1

  • 1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces an Android app for Saudi Sign Language (SSL) translation, bridging communication gaps for deaf individuals. The deep learning-powered tool translates SSL to text and audio, enhancing accessibility.

Keywords:
CNNMobileNetdeep learningpose estimationsaudi sign languagesign language recognition

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Deaf and hard-of-hearing individuals primarily use sign language for communication.
  • A significant communication barrier exists due to the scarcity of automatic sign language translation tools, particularly for Arabic Sign Language (ArSL) and Saudi Sign Language (SSL).
  • Existing vision-based sign language recognition systems predominantly focus on non-Arabic sign languages.

Purpose of the Study:

  • To develop a mobile application for efficient communication assistance for deaf and hard-of-hearing individuals in Saudi Arabia.
  • To create a tool that translates Saudi Sign Language (SSL) into text and audio using deep learning.
  • To offer unique features not found in existing ArSL translation applications.

Main Methods:

  • Development of an Android-based mobile application prototype.
  • Application of deep learning techniques for isolated Saudi Sign Language (SSL) recognition and translation.
  • Evaluation using a comprehensive dataset and user testing with both deaf and hearing individuals.

Main Results:

  • The proposed approach demonstrated superior performance compared to several state-of-the-art methods.
  • The translation accuracy achieved was comparable to existing advanced approaches.
  • User testing confirmed the practical usefulness of the mobile application for both deaf and hearing users.

Conclusions:

  • The developed mobile application effectively translates Saudi Sign Language (SSL) to text and audio.
  • The tool significantly enhances communication accessibility for the deaf community in Saudi Arabia.
  • Future work will focus on improving model accuracy and expanding application features.