Study of Sign Language Alphabet Recognition Based on Sign Language Visual Word Features
Yang Qua · Jisuanji gongcheng · 2014
In order to effectively recognize the sign language alphabet, this paper presents an algorithm based on Sign Language Visual Word(SLVW). It uses Kinect to obtain the video and depth image information of sign language gestures, calculates spindle direction angle and mass center position of the depth image to adjust the search window and for gesture tracking which depends on depth image information DI_CamShift. An Ostu method based on depth integral image is used to gesture segmentation, and the Scale Invariant Feature Transform(SIFT) data are extracted. It generates SLVW from small regions represented by local feature descriptors. After counting the frequency of visual words in a sign language alphabet image, it builds Bag of Words(BoW) to describe manual alphabets and uses Support Vector Machine(SVM) for recognition. Experimental results show that this method has high recognition accuracy and good robustness. Meanwhile, all of color, light and shadow have no effect on it. The average recognition rate of 30 sign language alphabets in the sign language video under complex background is 96.21%.