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Automated sign language detection and classification using reptile search algorithm with hybrid deep learning.

Hadeel Alsolai1, Leen Alsolai1, Fahd N Al-Wesabi2

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Heliyon
|December 27, 2023
PubMed
Summary

This study presents a new automated sign language recognition system using hybrid deep learning and a reptile search algorithm. The SLDC-RSAHDL technique improves sign language detection and classification accuracy for better accessibility.

Keywords:
Computer visionDeep learningIntelligent modelsReptile search algorithmSign language

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Sign language recognition (SLR) facilitates communication for deaf and hard-of-hearing individuals.
  • Existing SLR methods face challenges like sign variability, complexity, and real-time processing demands.
  • Deep learning (DL), computer vision (CV), and machine learning (ML) are key technologies in SLR.

Purpose of the Study:

  • To introduce an automated sign language detection and classification system (SLDC-RSAHDL).
  • To enhance SLR accuracy and efficiency using hybrid deep learning and metaheuristic optimization.
  • To address the limitations of current sign language recognition technologies.

Main Methods:

  • Utilizing MobileNet as a feature extractor for sign language gestures.
  • Employing Manta Ray Foraging Optimization (MRFO) to tune MobileNet hyperparameters.
  • Implementing a Hybrid Deep Learning (HDL) model combining Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) for classification.
  • Applying the Reptile Search Algorithm (RSA) for optimal hyperparameter selection in the HDL model.

Main Results:

  • The SLDC-RSAHDL technique demonstrated improved detection and classification rates for sign language.
  • Experimental results showed superior performance compared to existing deep learning techniques.
  • The hybrid deep learning approach with metaheuristic optimization enhanced recognition accuracy.

Conclusions:

  • The SLDC-RSAHDL system offers a promising advancement in automated sign language recognition.
  • This approach effectively addresses key challenges in sign language interpretation.
  • The study highlights the potential of integrating metaheuristic algorithms with deep learning for improved SLR systems.