mlsanityData to 14 Oct 2026

Sign language recognition from skeletal data using graph and recurrent neural networks

arXiv 2511.05772 ↗First posted 8 Nov 2025cs.CV cs.AI cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

This work presents an approach for recognizing isolated sign language gestures using skeleton-based pose data extracted from video sequences. A Graph-GRU temporal network is proposed to model both spatial and temporal dependencies between frames, enabling accurate classification. The model is trained and evaluated on the AUTSL (Ankara university Turkish sign language) dataset, achieving high accuracy. Experimental results demonstrate the effectiveness of integrating graph-based spatial representations with temporal modeling, providing a scalable framework for sign language recognition. The results of this approach highlight the potential of pose-driven methods for sign language understanding.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 8 Nov 2025.

Builds on

18 of 33 references.

References · 18

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  4. 2017Graph Attention Networkscited by 8,923
  5. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  6. 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,162
  7. 2017A Closer Look at Spatiotemporal Convolutions for Action Recognitioncited by 1,143
  8. 2015Empirical Evaluation of Rectified Activations in Convolutional Networkcited by 519
  9. 2013DeepPose: Human Pose Estimation via Deep Neural Networkscited by 305
  10. 2022Two-Stream Network for Sign Language Recognition and Translationcited by 123
  11. 2021Skeleton Aware Multi-modal Sign Language Recognitioncited by 101
  12. 2020AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methodscited by 75
6 more
  1. 2023Sign Language Translation from Instructional Videoscited by 39
  2. 2020Pose-based Sign Language Recognition using GCN and BERTcited by 37
  3. 2021Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognitioncited by 25
  4. 2019Spatial-Temporal Graph Convolutional Networks for Sign Language Recognitioncited by 23
  5. 2023Isolated Sign Language Recognition based on Tree Structure Skeleton Imagescited by 15
  6. 2021Context Matters: Self-Attention for Sign Language Recognitioncited by 5

Built on by

Not cited yet.