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Fingerspelling Detection in American Sign Language

Fingerspelling Detection in American Sign Language

Authors :
Diane Brentari
Greg Shakhnarovich
Bowen Shi
Karen Livescu
Source :
CVPR
Publication Year :
2021
Publisher :
IEEE, 2021.

Abstract

Fingerspelling, in which words are signed letter by letter, is an important component of American Sign Language. Most previous work on automatic fingerspelling recognition has assumed that the boundaries of fingerspelling regions in signing videos are known beforehand. In this paper, we consider the task of fingerspelling detection in raw, untrimmed sign language videos. This is an important step towards building real-world fingerspelling recognition systems. We propose a benchmark and a suite of evaluation metrics, some of which reflect the effect of detection on the downstream fingerspelling recognition task. In addition, we propose a new model that learns to detect fingerspelling via multi-task training, incorporating pose estimation and fingerspelling recognition (transcription) along with detection, and compare this model to several alternatives. The model outperforms all alternative approaches across all metrics, establishing a state of the art on the benchmark.<br />Comment: CVPR 2021

Details

Database :
OpenAIRE
Journal :
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Accession number :
edsair.doi.dedup.....7a609b57c1eaeb4789f181a69e1de8a2
Full Text :
https://doi.org/10.1109/cvpr46437.2021.00415