  The recognition of sign language is a challenging task with an important role
in society to facilitate the communication of deaf 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.
