Liwicki, S. and Everingham, M. (2009) Automatic recognition of fingerspelled words in British Sign Language. In: Proceedings of CVPR4HB'09. 2nd IEEE Workshop on CVPR for Human Communicative Behavior Analysis, Thursday June 25th, 2009, Miami, Florida. , pp. 50-57.
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%.
|Institution:||The University of Leeds|
|Academic Units:||The University of Leeds > Faculty of Engineering (Leeds) > School of Computing (Leeds)|
|Depositing User:||Miss Jamie Grant|
|Date Deposited:||07 Jul 2009 11:35|
|Last Modified:||16 Sep 2016 13:47|