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Preprocessing for Keypoint-Based Sign Language Translation without Glosses.

Youngmin Kim1, Hyeongboo Baek1

  • 1Department of Computer Science and Engineering, Incheon National University (INU), Incheon 22012, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a novel sign language translation (SLT) method using skeleton points, bypassing costly gloss annotations. The approach enhances robustness and minimizes information loss for improved deaf communication technology.

Keywords:
computer visiondeep learningsign language translationvideo processing

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Spoken language translation technology is advanced, but sign language translation (SLT) lags.
  • Obtaining gloss annotations for sign language datasets is expensive and time-consuming.
  • Limited research exists for SLT, particularly for deaf individuals.

Purpose of the Study:

  • To propose a novel sign language video-processing method for SLT that does not require gloss annotations.
  • To develop a robust SLT model resilient to background noise and variations in signer body length.
  • To minimize video information loss during the translation process.

Main Methods:

  • Leveraging signer's skeleton points to identify movements for model building.
  • Implementing a keypoint normalization process to preserve movements and account for body length variations.
  • Utilizing a stochastic frame selection technique to prioritize important video frames.

Main Results:

  • The proposed method demonstrates effectiveness on German and Korean sign language datasets.
  • Quantitative experiments show robust performance across various metrics.
  • The approach successfully translates sign language without relying on gloss annotations.

Conclusions:

  • The developed gloss-free SLT method offers a viable and cost-effective solution.
  • The technique enhances the robustness and efficiency of sign language translation systems.
  • This research contributes to advancing communication accessibility for the deaf community.