Automatic recognition of fingerspelled words in British sign language
Stephan Liwicki and Mark Everingham
In: 2nd IEEE Workshop on CVPR for Human Communicative Behavior Analysis (CVPR4HB'09), 25 Jun 2009, Miami, USA.
We investigate the problem of recognizing words from video, fingerspelled using the British Sign Language (BSL) fingerspelling alphabet. This is a challenging task since the BSL alphabet involves both hands occluding each other, and
contains signs which are ambiguous from the observer’s
viewpoint. The main contributions of our work include:
(i) recognition based on hand shape alone, not requiring
motion cues; (ii) robust visual features for hand shape
recognition; (iii) scalability to large lexicon recognition
with no re-training.
We report results on a dataset of 1,000 low quality webcam
videos of 100 words. The proposed method achieves a
word recognition accuracy of 98.9%.
|EPrint Type:||Conference or Workshop Item (Poster)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Mark Everingham|
|Deposited On:||08 March 2010|