Affiliations: School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, Zhejiang, China
Correspondence:
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Corresponding author: Qiuhong Tian, %****␣thc-29-thc192000_temp.tex␣Line␣25␣**** School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China. E-mail: tianqiuhong@zstu.edu.cn.
Abstract: BACKGROUND: For a traditional vision-based static sign language recognition (SLR) system, arm segmentation is a major factor restricting the accuracy of SLR. OBJECTIVE: To achieve accurate arm segmentation for different bent arm shapes, we designed a segmentation method for a static SLR system based on image processing and combined it with morphological reconstruction. METHODS: First, skin segmentation was performed using YCbCr color space to extract the skin-like region from a complex background. Then, the area operator and the location of the mass center were used to remove skin-like regions and obtain the valid hand-arm region. Subsequently, the transverse distance was calculated to distinguish different bent arm shapes. The proposed segmentation method then extracted the hand region from different types of hand-arm images. Finally, the geometric features of the spatial domain were extracted and the sign language image was identified using a support vector machine (SVM) model. Experiments were conducted to determine the feasibility of the method and compare its performance with that of neural network and Euclidean distance matching methods. RESULTS: The results demonstrate that the proposed method can effectively segment skin-like regions from complex backgrounds as well as different bent arm shapes, thereby improving the recognition rate of the SLR system.
Keywords: Static sign language recognition, image segmentation, bent arm shape, transverse distance, geometric feature
