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

AAAI 2024 pp. 23606-23607

doi:10.1609/AAAI.V38I21.30492 /aaai/2024/pallickara2024aaai-wireframe/

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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Cite

Text

Pallickara and Sreedharan. "A Wireframe-Based Approach for Classifying and Acquiring Proficiency in the American Sign Language (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30492

Markdown

[Pallickara and Sreedharan. "A Wireframe-Based Approach for Classifying and Acquiring Proficiency in the American Sign Language (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/pallickara2024aaai-wireframe/) doi:10.1609/AAAI.V38I21.30492

BibTeX

@inproceedings{pallickara2024aaai-wireframe,
  title     = {{A Wireframe-Based Approach for Classifying and Acquiring Proficiency in the American Sign Language (Student Abstract)}},
  author    = {Pallickara, Dylan and Sreedharan, Sarath},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {23606-23607},
  doi       = {10.1609/AAAI.V38I21.30492},
  url       = {https://mlanthology.org/aaai/2024/pallickara2024aaai-wireframe/}
}