Recognizing 3D Continuous Letter Trajectory Gesture Using Dynamic Time Warping

Abstract

Letter trajectory gesture recognition is widely used in Human Computer Interaction. Many approaches for letter trajectory gesture recognition have been proposed in the past several years. Most of the traditional approaches detect letters based on the beginning/end points provided by the user. It causes low writing speed and uncomfortable writing experience. Moreover, traditional Dynamic Time Warping cannot classify the letters which have the familiar trajectory. In this paper, we combine Dynamic Time Warping with structured points of letters to overcome those problems. The main contribution of this paper is that we introduce the structured points information of letters in Time Warping process to detect letters from hand trajectories. Based on this, we can successfully recognize the letter from the weak inter-class feature and the continuous trajectory without beginning point and end point given by the user. Furthermore, we can handle the self-contained trajectory based on the complexity of letters. We evaluate this system in our gesture dataset, and it shows that the proposed approach can significantly outperform the traditional begin-end gesture approach.