Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison
Australian Centre for Robotic Vision · Australian National University
Abstract
Vision-based sign language recognition aims at helping the deaf people to communicate with others. However, most existing sign language datasets are limited to a small number of words. Due to the limited vocabulary size, models learned from those datasets cannot be applied in practice. In this paper, we introduce a new large-scale Word-Level American Sign Language (WLASL) video dataset, containing more than 2000 words performed by over 100 signers. This dataset will be made publicly available to the research community. To our knowledge,it is by far the largest public ASL dataset to facilitate word-level sign recognition research. Based on this new large-scale dataset, we are able to experiment with several…
Citation impact
- FWCI
- 42.85
- Percentile
- 100%
- References
- 94
Authors
4- DLDongxu LiCorresponding
Australian Centre for Robotic Vision, Australian National University
- CRCristian Rodriguez Opazo
Australian National University, Australian Centre for Robotic Vision
- XYXin Yu
Australian Centre for Robotic Vision, Australian National University
- HLHongdong Li
Australian National University, Australian Centre for Robotic Vision
Topics & keywords
- Computer science
- Sign language
- Artificial intelligence
- Benchmarking
- Vocabulary
- Word (group theory)
- Language model
- Scale (ratio)
- Quality Education