# Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition

## TL;DR

This paper introduces a novel Spatial-Temporal Graph Convolutional Network for sign language recognition, leveraging skeletal movement data to improve understanding of complex sign language dynamics.

## Contribution

It proposes a new graph-based neural network model and provides a new dataset of human skeletons for sign language, advancing research in this area.

## Key findings

- Effective modeling of sign language dynamics using graphs
- Introduction of a new skeletal dataset for sign language
- Potential improvements in sign language recognition accuracy

## Abstract

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.