Automatic hand gesture segmentation for recognition of Vietnamese sign language

In this paper, we propose a new solution to identify hand gestures corresponding to alphabetic characters of Vietnamese Sign Language (VSL) via a sequence of images (video) collected from the depth sensor in a Microsoft Kinect. First, a preprocessing is performed to localize and separate the hand from each image and then remove possible noise. In the next stage, the object is extracted to select key frames, which support to represent a segment of the video. Each determined key frame is then converted to a binary image and estimate some biological information such as the hand boundary, finger positions and the palm center. The position of palm centre and fingertips are also localized in 3D space. The process of recognition is performed using Support Vector Machine (SVM) method. The experiments show that the proposed approach is promising since the recognition accuracy is about 91%. � 2016 ACM.