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Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition

arXiv 1901.11164 ↗First posted 31 Jan 2019cs.LG cs.CV stat.MLcited by 23

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Abstract (arXiv)

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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jan 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 5; 2023: 4; 2024: 4; 2025: 5; 2026: 1.

23 papers.

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  1. 2021Skeleton Aware Multi-modal Sign Language Recognitioncited by 101
  2. 2023CVT-SLR: Contrastive Visual-Textual Transformation for Sign Language Recognition with Variational Alignmentcited by 44
  3. 2021Sign Language Recognition via Skeleton-Aware Multi-Model Ensemblecited by 16
  4. 2023Isolated Sign Language Recognition based on Tree Structure Skeleton Imagescited by 15
  5. 2021Skeletal Graph Self-Attention: Embedding a Skeleton Inductive Bias into Sign Language Productioncited by 11
  6. 2022ASL Video Corpora & Sign Bank: Resources Available through the American Sign Language Linguistic Research Project (ASLLRP)cited by 10
  7. 2025Survey on Hand Gesture Recognition from Visual Inputcited by 10
  8. 2024A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalitiescited by 8
  9. 2022Leveraging Graph-based Cross-modal Information Fusion for Neural Sign Language Translationcited by 4
  10. 2023GestSync: Determining who is speaking without a talking headcited by 4
  11. 2024Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced Datasetscited by 4
  12. 2021Word-level Sign Language Recognition with Multi-stream Neural Networks Focusing on Local Regions and Skeletal Informationcited by 3
11 more
  1. 2022ASL-Skeleton3D and ASL-Phono: Two Novel Datasets for the American Sign Languagecited by 1
  2. 2022Combining Efficient and Precise Sign Language Recognition: Good pose estimation library is all you needcited by 1
  3. 2023Learning from What is Already Out There: Few-shot Sign Language Recognition with Online Dictionariescited by 1
  4. 2024A Transformer-Based Multi-Stream Approach for Isolated Iranian Sign Language Recognitioncited by 1
  5. 2025SignBart -- New approach with the skeleton sequence for Isolated Sign language Recognitioncited by 1
  6. 2022Graph Neural Network based Child Activity Recognitioncited by 0
  7. 2024New keypoint-based approach for recognising British Sign Language (BSL) from sequencescited by 0
  8. 2025Skeleton-based sign language recognition using a dual-stream spatio-temporal dynamic graph convolutional networkcited by 0
  9. 2025Sign language recognition from skeletal data using graph and recurrent neural networkscited by 0
  10. 2025RoCoISLR: A Romanian Corpus for Isolated Sign Language Recognitioncited by 0
  11. 2026The In-Car Sign Language Corpus (ICSL): A Multi-Modal Resource for Constrained-Space Sign Language Recognitioncited by 0