Research on sign language gesture division and gesture extraction in complex background

Abstract

Sign language plays an important role in information transmission and emotional communication between deaf-mute people and the outside world. With the development of artificial intelligence technology, the recognition, translation and generation of sign language based on digital image processing have attracted worldwide attention. In the field of sign language recognition, effective hand division and gesture extraction are the first and key steps, which directly affect the accuracy of sign language recognition. In this paper, a hand information extraction method based on depth image processing is proposed to solve the problem of sign language gesture extraction in complex background. For sign language speakers,hands are at the front of their bodies, so the depth images of sign language speakers can be collected by depth camera, and the complex background can be removed and hand information can be extracted by segmenting different color objects in the depth images. In this paper, the D435i camera of Inter is used to capture the depth image of the sign language speaker, and the HSV color space model based on the digital image is used for threshold processing of the fusion of hue components and brightness components to achieve the division of the hand position; through median filtering and mathematical morphology of digital image, division noise is removed and interference is reduced. Through skeleton extraction algorithm, the gesture gesture can be obtained. Experiments show that the proposed acquisition scheme and algorithm flow in this paper can effectively realize hand position division and gesture extraction in complex background conditions, and provide a good foundation for subsequent gesture recognition and expression.