A Sign Language Recognition System in Complex Background

In view of the complicity of background, similarity of hand shape and the limitations of the algorithm, we propose a new system for sign language recognition. To separate gesture from complex backgrounds we use initial division based on improved color clustering and the re-segmentation by graph cut method. After that, the outline of hand shape is detected by CV model, the convex defects are found, the Hu moments and the geometric features are calculated. Finally, utilizing the SVM to classification that consists of the first classification on the number of defects and the second classification through multi-feature fusion, the average recognition rate of 26 kinds of sign language is 91.18 % in our collection of images which shows the effectiveness of the proposed algorithms.