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Real-Time Arabic Sign Language Recognition Using a Hybrid Deep Learning Model.

Talal H Noor1, Ayman Noor1, Ahmed F Alharbi1

  • 1Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah 42353, Saudi Arabia.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
Summary
This summary is machine-generated.

This study developed a deep learning model to recognize Arabic Sign Language (ArSL), addressing interpreter shortages for the hearing-impaired community in Saudi Arabia. The hybrid CNN-LSTM model achieved high accuracy, improving communication accessibility.

Keywords:
Arabic sign language recognitionCNNsLSTMdeep learningreal-time detection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Sign language is crucial for deaf individuals, but interpreter shortages, particularly for Arabic Sign Language (ArSL) in Saudi Arabia, limit access to services.
  • This accessibility gap disproportionately affects the hearing-impaired population, hindering their participation in public life.
  • Technological solutions are needed to bridge communication barriers faced by the deaf community.

Purpose of the Study:

  • To develop and evaluate a deep learning-based system for recognizing Arabic Sign Language (ArSL).
  • To address the critical shortage of human interpreters for ArSL, thereby enhancing communication accessibility for the hearing-impaired in Saudi Arabia.
  • To leverage artificial intelligence to create a more inclusive environment for individuals with hearing disabilities.

Main Methods:

  • A hybrid deep learning model combining a Convolutional Neural Network (CNN) for spatial feature extraction and a Long Short-Term Memory (LSTM) network for spatio-temporal analysis of sign language gestures.
  • Development of a custom dataset comprising 4000 images for 10 static ArSL gestures and 500 videos for 10 dynamic ArSL gestures.
  • Training and evaluation of the hybrid CNN-LSTM model on the created ArSL dataset.

Main Results:

  • The CNN classifier achieved an accuracy of 94.40% in extracting spatial features from ArSL data.
  • The LSTM classifier demonstrated an accuracy of 82.70% in capturing sequential and temporal aspects of ArSL, including hand movements.
  • The hybrid model showed promising performance in recognizing both static and dynamic ArSL gestures.

Conclusions:

  • The proposed hybrid deep learning model offers a viable technological solution to mitigate the shortage of Arabic Sign Language interpreters.
  • This approach significantly enhances communication accessibility for the hearing-impaired community in Saudi Arabia.
  • The study represents a significant advancement in promoting inclusivity and improving the quality of life for deaf individuals through AI-driven communication tools.