ArticleSensors (Basel, Switzerland)2022
Spatial Attention-Based 3D Graph Convolutional Neural Network for Sign Language Recognition.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- A Comprehensive Review of SLR Systems: Challenges, Datasets, and Unresolved Gaps.Journal of imaging · 2026Review
- A comparative analysis of video vision transformers on word-level sign language datasets.PloS one · 2026Article
- Towards a Lightweight Arabic Sign Language Translation System.Sensors (Basel, Switzerland) · 2025Article
- Re-Evaluation Method by Index Finger Position in the Face Area Using Face Part Position Criterion for Sign Language Recognition.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
14 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.