For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Recognition of Continuous Korean Sign Language Using Gesture Tension Model and Soft Computing Technique
Jung-Bae KIM Zeungnam BIEN
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2004/05/01
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Human-computer Interaction
sign language recognition, gesture recognition, continuous gesture, gesture tension model, soft computing.,
Full Text: PDF>>
We present a method for recognition of continuous Korean Sign Language (KSL). In the paper, we consider the segmentation problem of a continuous hand motion pattern in KSL. For this, we first extract sign sentences by removing linking gestures between sign sentences. We use a gesture tension model and fuzzy partitioning. Then, each sign sentence is disassembled into a set of elementary motions (EMs) according to its geometric pattern. The hidden Markov model is adopted to classify the segmented individual EMs.