EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 85 (2026) ITM Web Conf., 85 (2026) 03005 References
Open Access
Issue
ITM Web Conf.
Volume 85, 2026
Intelligent Systems for a Sustainable Future (ISSF 2026)
Article Number 03005
Number of page(s) 8
Section Data Science, IoT, Optimization & Predictive Analytics
DOI https://doi.org/10.1051/itmconf/20268503005
Published online 09 April 2026
  1. R. Rastgoo, K. Kiani, and S. Escalera, "Sign language recognition: A deep survey," Expert Systems with Applications, vol. 164, 2021, Art. no. 113794. https://doi.Org/10.1016/j.eswa.2020.113794 [Google Scholar]
  2. S. Alyami and A. Alharbi, "Continuous sign language recognition: A systematic review of recent deep learning approaches," Image and Vision Computing, vol. 140, 2024, Art. no. 104857. [Google Scholar]
  3. A. Kumar and S. Sharma, "Indian sign language recognition using convolutional neural networks," IEEE Access, vol. 10, pp. 48231–48242, 2022. [Google Scholar]
  4. M. Singh, R. Gupta, and P. Jain, "Dynamic gesture recognition using 3D convolutional neural networks," Multimedia Tools and Applications, vol. 82, no. 12, pp. 18345–18362, 2023. [Google Scholar]
  5. P. Buttar and H. Singh, "Hybrid CNN-LSTM architecture for continuous sign language recognition," Applied Sciences, vol. 13, no. 4, 2023, Art. no. 2147. [Google Scholar]
  6. Y. Liu, J. Zhang, and L. Wang, "Transformer-based continuous sign language recognition with spatio-temporal attention," Pattern Recognition Letters, vol. 165, pp. 55–63, 2024. [Google Scholar]
  7. K. Papadimitriou et al., "Multimodal locally enhanced transformer for continuous sign language recognition," in Proc. Interspeech, 2023, pp. 2458–2462. [Google Scholar]
  8. Indian Sign Language Research and Training Centre (ISLRTC), Indian Sign Language Dictionary and Standardization Report, New Delhi, India, 2021. [Google Scholar]
  9. V. Sharma and A. Mehta, "Dataset analysis for Indian sign language recognition systems," Journal of Visual Communication and Image Representation, vol. 89, 2022, Art. no. 103672. [Google Scholar]
  10. iSign Consortium, "iSign: A benchmark dataset for Indian sign language processing," arXiv preprint arXiv:2407.05404, 2024. [Google Scholar]
  11. R. Narayanan and K. Subramanian, "Challenges in multilingual natural language processing for Indian languages," ACM Computing Surveys, vol. 56, no. 2, 2023, Art. no. 29. [Google Scholar]
  12. P. Rao, S. Deepa, and V. Peguda, "Speech-to-sign language translation for Indian languages using neural models," Procedia Computer Science, vol. 215, pp. 614–621, 2024. [Google Scholar]
  13. M. J. Page et al., "The PRISMA 2020 statement: An updated guideline for reporting systematic reviews," BMJ, vol. 372, 2021, Art. no. n71. [Google Scholar]
  14. J. Camgoz, S. Hadfield, O. Koller, and R. Bowden, "Neural sign language translation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 6, pp. 2065–2079, 2021. [Google Scholar]
  15. O. Koller, "Quantitative survey of the state of the art in sign language recognition," Autonomous Agents and Multi-Agent Systems, vol. 34, no. 1, 2020. [Google Scholar]
  16. J. Li, W. Xu, and Y. Wang, "Sign language recognition using graph convolutional networks," IEEE Access, vol. 9, pp. 123456–123467, 2021. [Google Scholar]
  17. H. Ko et al., "OpenHands: Making sign language recognition accessible with pose-based modelling," in Proc. CVPR Workshops, 2022. [Google Scholar]
  18. F. Yin et al., "Inclusive attention-based sign language recognition," Pattern Recognition, vol. 128, 2022. [Google Scholar]
  19. T. Saunders et al., "Adversarial training for robust sign language recognition," Computer Vision and Image Understanding, vol. 215, 2022. [Google Scholar]
  20. L. Shi, Y. Zhang, J. Cheng, and H. Lu, "Skeleton-based action recognition with graph convolutional networks," IEEE Trans. Circuits Syst. Video Technol., vol. 32, no. 6, pp. 3456–3468, 2022. [Google Scholar]
  21. X. Hu et al., "Gloss-free end-to-end sign language translation," IEEE Trans. Multimedia, vol. 25, 2023. [Google Scholar]
  22. R. S. Kumar and M. Rajalakshmi, "Vision-based Indian sign language recognition using deep residual networks," Multimedia Tools and Applications, vol. 82, 2023. [Google Scholar]
  23. Z. Zhou et al., "Transformer-based multi-scale temporal modelling for continuous sign language recognition," IEEE Access, vol. 12, 2024. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.