Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition
This work addresses communication barriers for deaf persons by improving sign language recognition, though it appears incremental as it builds on existing graph convolutional methods.
The authors tackled sign language recognition by proposing a Spatial-Temporal Graph Convolutional Network that captures skeletal movements in spatial and temporal dimensions, achieving results on a new dataset based on ASLLVD.
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.