Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition

Abstract--The recognition of sign language is a challenging task with an important role in society to facilitate the communication ofdeaf persons. We propose a new approach of Spatial-Temporal Graph Convolutional Network to sign language recognition based on the human skeletal movements. The method uses graphs to capture the signs dynamics in two dimensions, spatial and temporal, considering the complex aspects of the language. Additionally, we present a new dataset of human skeletons for sign language based on ASLLVD to contribute to future related studies. I. INTRODUCTION Sign language is a visual communication skill that enables individuals with different types of hearing impairment to communicate in society. It is the language used by most deaf people in their daily lives and, moreover, it is the symbol of identification between the members of that community and the main force that unites them. The sign language has a very close relationship with the culture of the country or even regions, and for this reason, each nation has its language [1]. According to the World Health Organization, the number of deaf people is about 466 million, and the organization estimates that by 2050 this number exceeds 900 million, which is equivalent to a forecast of 1 in 10 individuals around the world [2].