A Wireframe-Based Approach for Classifying and Acquiring Proficiency in the American Sign Language (Student Abstract)

Authors

  • Dylan Pallickara Poudre High School, Fort Collins, CO
  • Sarath Sreedharan Colorado State University, Fort Collins, CO

DOI:

https://doi.org/10.1609/aaai.v38i21.30492

Keywords:

AI Architectures, Human-Computer Interaction, Applications Of AI

Abstract

We describe our methodology for classifying ASL (American Sign Language) gestures. Rather than operate directly on raw images of hand gestures, we extract coor-dinates and render wireframes from individual images to construct a curated training dataset. This dataset is then used in a classifier that is memory efficient and provides effective performance (94% accuracy). Because we con-struct wireframes that contain information about several angles in the joints that comprise hands, our methodolo-gy is amenable to training those interested in learning ASL by identifying targeted errors in their hand gestures.

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Published

2024-03-24

How to Cite

Pallickara, D., & Sreedharan, S. (2024). A Wireframe-Based Approach for Classifying and Acquiring Proficiency in the American Sign Language (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23606-23607. https://doi.org/10.1609/aaai.v38i21.30492