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An integrated mediapipe-optimized GRU model for Indian sign language recognition.

Barathi Subramanian1, Bekhzod Olimov1, Shraddha M Naik1

  • 1School of Computer Science and Engineering, Kyungpook National University, Buk-gu, Daegu, 41566, South Korea.

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This study introduces a new MediaPipe-optimized gated recurrent unit (MOPGRU) model for improved Indian sign language recognition. The MOPGRU model enhances learning efficiency and prediction accuracy for sign language processing.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Sign language recognition faces challenges like hand gesture tracking, occlusion, and high computational costs.
  • Deep learning models struggle with long-term sequential data, leading to poor information processing and learning efficiency.

Purpose of the Study:

  • To propose an integrated MediaPipe-optimized gated recurrent unit (MOPGRU) model for enhanced Indian sign language recognition.
  • To address the limitations of existing sequential models in processing long-term data and capturing relevant information.

Main Methods:

  • Developed the MOPGRU model by modifying the update gate of standard GRU cells to discard redundant past information.
  • Incorporated feedback from the reset gate to focus on present input, enhancing attention.
  • Replaced hyperbolic tangent activation with exponential linear unit and SoftMax with Softsign in the output layer.

Main Results:

  • The MOPGRU model demonstrated superior prediction accuracy compared to other sequential models.
  • Achieved higher learning efficiency and improved information processing capabilities.
  • Exhibited faster convergence rates in sign language recognition tasks.

Conclusions:

  • The proposed MOPGRU model effectively overcomes challenges in sign language recognition.
  • Offers significant improvements in accuracy, efficiency, and processing capabilities for Indian sign language.
  • Represents a promising advancement in deep learning for sign language recognition systems.