Image and video for hearing impaired people
Journal on Image and Video Processing
Modelling and segmenting subunits for sign language recognition based on hand motion analysis
Pattern Recognition Letters
Novel boosting framework for subunit-based sign language recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Finding recurrent patterns from continuous sign language sentences for automated extraction of signs
The Journal of Machine Learning Research
Hidden Markov model for human to computer interaction: a study on human hand gesture recognition
Artificial Intelligence Review
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In sign language recognition, using subwords instead of whole signs as basic units will scale well with increasing vocabulary size. However, there are no subwords defined in the signs' lexical forms. How to automatically extract subwords is a challenging issue. In this paper, a novel approach is proposed to automatically extract these subwords from Chinese sign language(CSL). Signs can be broken down into several segments using hidden Markov models in which each state represents one segment. Temporal clustering algorithm is presented to extract subwords from these segments. The 238 subwords are automatically extracted from 5113 signs, and they can be used as the basic units for large vocabulary CSL recognition with good performance.