Improved Manual Alphabet Recognition Method Based on Multi-features Fusion

To realize the accurate recognition of manual alphabets in the sign language video,this paper presentes an improved algorithm based on DI_ CamShift(depth image CamShift) and SLVW(sign language visual word) multi-features combine. First,the video and depth image information of sign language gestures are obtained by usesing Kinect. Second,the spindle direction angle and mass center position of the depth images are calculated to adjust the search window and for gesture tracking. Third,an OTSU algorithm based on depth integral image is used to gesture segmentation,then both SIFT and Gabor features are extracted and fused by the CCA. Finally,the SLVW bag of words is built,and SVM is used for recognition. The best recognition rate of single manual alphabet can reach 99. 89%,and the average recognition rate is 96. 34%.