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Continuous Sign Language Recognition through a Context-Aware Generative Adversarial Network.

Ilias Papastratis1, Kosmas Dimitropoulos1, Petros Daras1

  • 1Visual Computing Lab at Information Technologies Institute of Centre for Research and Technology Hellas, VCL of CERTH/ITI Hellas, 57001 Thessaloniki, Greece.

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|April 30, 2021
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Summary
This summary is machine-generated.

This study introduces a novel generative adversarial network for continuous sign language recognition, improving accuracy by incorporating contextual information. The Sign Language Recognition Generative Adversarial Network (SLRGAN) enhances sign language translation for better communication.

Keywords:
continuous sign language recognitiongenerative adversarial networkssign language translation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Continuous sign language recognition is challenging due to the lack of temporal boundary information.
  • Existing methods often overlook text and contextual information, limiting recognition accuracy.
  • Deep generative models remain underexplored in sign language recognition.

Purpose of the Study:

  • To introduce a novel context-aware continuous sign language recognition approach.
  • To leverage generative adversarial networks (GANs) for improved sign language recognition.
  • To investigate the impact of contextual information on sign language translation accuracy.

Main Methods:

  • Developed a Sign Language Recognition Generative Adversarial Network (SLRGAN) with a generator and discriminator.
  • Generator extracts spatio-temporal features and incorporates contextual information (previous sentence hidden states) via BiLSTM.
  • Discriminator models text information at sentence and gloss levels to evaluate predictions.

Main Results:

  • Achieved word error rates of 23.4% on RWTH-Phoenix-Weather-2014.
  • Attained low word error rates of 2.1% on Chinese Sign Language (CSL) and 2.26% on Greek Sign Language (GSL) datasets.
  • Demonstrated the effectiveness of contextual information in improving recognition accuracy.

Conclusions:

  • The proposed SLRGAN effectively models data distribution and improves continuous sign language recognition.
  • Contextual information significantly enhances the accuracy of sign language translation.
  • The approach shows promise for both Deaf-to-Deaf and Deaf-to-hearing communication systems.