AI Article Synopsis
- Sign language is crucial for communication among hearing-impaired individuals, but mastering it can be challenging for those who can hear.
- The study introduces a novel sign language recognition architecture using a convolutional graph neural network (GCN) with fewer layers to reduce over-smoothing issues.
- The architecture includes a spatial attention mechanism to improve gesture representation, and its effectiveness is demonstrated through various dataset evaluations, showing impressive results.
Article Abstract
Sign language is the main channel for hearing-impaired people to communicate with others. It is a visual language that conveys highly structured components of manual and non-manual parameters such that it needs a lot of effort to master by hearing people. Sign language recognition aims to facilitate this mastering difficulty and bridge the communication gap between hearing-impaired people and others. This study presents an efficient architecture for sign language recognition based on a convolutional graph neural network (GCN). The presented architecture consists of a few separable 3DGCN layers, which are enhanced by a spatial attention mechanism. The limited number of layers in the proposed architecture enables it to avoid the common over-smoothing problem in deep graph neural networks. Furthermore, the attention mechanism enhances the spatial context representation of the gestures. The proposed architecture is evaluated on different datasets and shows outstanding results.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9227856 | PMC |
| http://dx.doi.org/10.3390/s22124558 | DOI Listing |
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