Improved dynamic gesture segmentation for Thai sign language translation

Sign languages normally make use of both static and dynamic gestures to achieve communication goal. In automated continuous gesture recognition, one of the problems found is how to separate a transitional gesture from a meaningful dynamic gesture. In addition, a variation in speed, absolute position and orientation of a dynamic gesture makes an automatic recognition task nontrivial. This paper proposes a segmentation method that alleviates these problems. The method is based on the fact that many (meaningful) dynamic gestures as appeared in Thai and other sign languages, are performed in an approximately (quasi) periodic manner. Therefore, such dynamic gestures can be distinguished from a transitional gesture by means of Fourier analysis, performed on samples (discrete signal) of hand shapes, positions and orientation. The analysis, however, requires a good choice of data sample windowing. This paper describes a preprocessing method for estimation of the dynamic gesture data windowing period. The proposed method helps improving the accuracy of segmenting hand shape and location data samples into static, periodic and nonperiodic gestures. In addition, accuracy of the feature extracted from the Fourier analysis of a dynamic gesture signal is improved, thus increasing the recognition rate. Experimental results performed on a real data are included.