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Fingerspelling Detection in American Sign Language
Fingerspelling Detection in American Sign Language
- 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
- Subjects :
- FOS: Computer and information sciences
Computer Science - Computation and Language
American Sign Language
Computer science
business.industry
Computer Vision and Pattern Recognition (cs.CV)
Speech recognition
Computer Science - Computer Vision and Pattern Recognition
Sign language
language.human_language
Task (project management)
Gesture recognition
language
Benchmark (computing)
Artificial intelligence
Transcription (software)
business
Computation and Language (cs.CL)
Pose
Fingerspelling
Subjects
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