Enhancing Language Learning with Real-Time Sign Language Recognition and Feedback

Despite the significance of Sign Language education, access to resources with immediate feedback remains a challenge. This study aims to assess the effectiveness of an online learning tool offering real-time video feedback, accessible via basic laptops. Leveraging the capabilities of Google’s Media Pipe software and the project’s robust logic, the prototype aims to provide learners with immediate and accurate feedback, addressing the challenge of access to effective resources for American Sign Language (ASL) education. Focusing on the ASL alphabet, computer-vision based learning tools for static and dynamic signs will be analyzed to explore the tool’s capabilities and limitations.

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Publication Details

Published
2024-10-11
DOI
https://doi.org/10.1109/urtc65039.2024.10937578
Citations
1
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.37
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article

Enhancing Language Learning with Real-Time Sign Language Recognition and Feedback

Saurav Basnet, Yassine Habchi, Christel Daniel, Nasr M. Liam
1 citations
Hand Gesture Recognition Systems
0.37
article

Enhancing Language Learning with Real-Time Sign Language Recognition and Feedback

Saurav Basnet, Yassine Habchi, Christel Daniel, Nasr M. Liam
article en
1 citations

Abstract

Despite the significance of Sign Language education, access to resources with immediate feedback remains a challenge. This study aims to assess the effectiveness of an online learning tool offering real-time video feedback, accessible via basic laptops. Leveraging the capabilities of Google’s Media Pipe software and the project’s robust logic, the prototype aims to provide learners with immediate and accurate feedback, addressing the challenge of access to effective resources for American Sign Language (ASL) education. Focusing on the ASL alphabet, computer-vision based learning tools for static and dynamic signs will be analyzed to explore the tool’s capabilities and limitations.

Wentworth Institute of Technology (US)
Quality Education
Openalex Percentile: Top 34%
Hand Gesture Recognition Systems
0.37
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