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Explainable AI for sign language recognition models: Integrating Grad-Cam LIME and Integrated Gradients.

Fatima-Zahrae El-Qoraychy1, Yazan Mualla1, Hui Zhao2

  • 1Université de Technologie de Belfort Montbéliard, UTBM, CIAD UR 7533, Belfort, France.

Plos One
|December 10, 2025
PubMed
Summary

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This summary is machine-generated.

This study enhances sign language recognition using hand masks and Explainable Artificial Intelligence (XAI). The mask-based model improves accuracy by focusing on hand structure, making assistive technologies more reliable.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Sign language recognition is vital for communication between hearing and deaf communities.
  • Existing models often struggle with noise and lack transparency.

Purpose of the Study:

  • To enhance the robustness and explainability of a VGG19-based sign language classification model.
  • To introduce a segmentation-based approach using hand masks and validate it with Explainable Artificial Intelligence (XAI).

Main Methods:

  • Dataset augmentation and alternative data representations.
  • A segmentation-based approach using U-Net generated hand masks, replacing depth images.
  • Explainable Artificial Intelligence (XAI) methods including Grad-CAM, LIME, and integrated gradients for model interpretation.

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Main Results:

  • The mask-based model demonstrated improved classification accuracy compared to depth images by focusing on hand shape and structure.
  • Comparative analysis showed RGB models capture texture/color, while mask-based models focus on essential hand features.
  • XAI methods validated results, highlighting influential image regions and enabling multi-perspective analysis.

Conclusions:

  • The enhanced model improves generalization and explainability for American Sign Language recognition.
  • The mask-based approach with XAI integration increases transparency and reliability in assistive technologies.
  • This research fosters greater trust and usability in sign language recognition systems.