Open Access
Issue
MATEC Web Conf.
Volume 417, 2025
2025 RAPDASA-RobMech-PRASA-AMI Conference: Bridging the Gap between Industry & Academia - The 26th Annual International RAPDASA Conference, joined by RobMech, PRASA and AMI, co-hosted by CSIR and Tshwane University of Technology, Pretoria
Article Number 10004
Number of page(s) 13
Section Pattern Recognition
DOI https://doi.org/10.1051/matecconf/202541710004
Published online 25 November 2025
  1. W.H.O, Deafness, https://www.who.int/news-room/facts-in-pictures/detail/deafness (2024) [Google Scholar]
  2. Sign Solutions, What are the different types of sign language?, https://www.signsolutions.uk.com/what-are-the-different-types-of-sign-language/ (2024) [Google Scholar]
  3. B. Shi, X. Chen, Z. He, R. Han, Development of magnetic-sensor-based hand gesture recognition system for sign language, in Proceedings of the IEEE International Electrical and Energy Conference. (CIEEC), Hefei, China, 12-14 May (2023), 2302–2305. [Google Scholar]
  4. M. Bansal, S. Gupta, Detection and recognition of hand gestures for Indian Sign Language recognition system, in Proceedings of the IEEE International Conference on Signal Processing, Computing and Control (ISPCC), Solan, India, 07-09 October (2021), 136–140. [Google Scholar]
  5. K. Tripathi, N. Baranwal, G.C. Nandi, Continuous dynamic Indian Sign Language gesture recognition with invariant backgrounds, in Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI), Kochi, India, 10-13 August (2015), 2211–2216. [Google Scholar]
  6. P.S. Neethu, R. Suguna, D. Sathish, An efficient method for human hand gesture detection and recognition using deep learning convolutional neural networks. Soft Comput. 24, 15239–15248 (2020). https://doi.org/10.1007/s00500-024-10038-0 [Google Scholar]
  7. H.D. Alon, M.A. Ligayo, M.P. Melegrito, C.F. Cunanan, E.E. Uy II, Deep-hand: A deep inference vision approach of recognising a hand sign language using American alphabet, in Proceedings of the IEEE International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Dubai, United Arab Emirates, 17-18 March (2021), 373–377. [Google Scholar]
  8. A. Jain, A. Sethi, D.K. Vishwakarma, A. Jain, Ensembled neural network for static hand gesture recognition, in Proceedings of the IEEE in Proceedings of the 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, 06-08 July (2021), 1–5. [Google Scholar]
  9. B. Alsharif, L. Peng, M. Mohaisen, F. Alabdulmohsin, Real-time American Sign Language interpretation using deep learning and key-point tracking. Sensors 25, 2138 (2025). https://doi.org/10.3390/s25072138 [Google Scholar]
  10. M. Aly, I.S. Fathi, Recognising American Sign Language gestures efficiently and accurately using a hybrid transformer model. Sci. Rep. 15, 20253 (2025). [Google Scholar]
  11. J. Rheiner, T. Dietz, A. Kroll, From pixels to letters: A high-accuracy CPU-real-time American Sign Language detection pipeline. Mach. Learn. Appl. 20, 100650 (2025). https://doi.org/10.1016/j.mlwa.2025.100650 [Google Scholar]
  12. S. Sharma, R. Gupta, A. Kumar, A TinyML solution for an IoT-based communication device for hearing impaired. Expert Syst. Appl. 246, 123147 (2024). https://doi.org/10.1016/j.eswa.2024.123147 [Google Scholar]
  13. P. Muralidhar, A. Saha, P. Sateesh, Customisable dynamic hand gesture recognition system for motor impaired people using siamese neural network, in Proceedings of the IEEE International Conference of Artificial Intelligence and Information Technology (ICAIIT), Yogyakarta, Indonesia, 13-15 March (2019), 354–358. [Google Scholar]
  14. S. Adesola, TinyML on Arduino Nano 33 BLE Sense with Teachable Machine, https://github.com/adesolasamuel/TinyML-on-Arduino-Nano-33-BLE-Sense-with-Teachable-Machine (2024) [Google Scholar]
  15. Z. Wang, B. Chen, J. Wu, Effective Inertial Hand Gesture Recognition Using Particle Filtering Based Trajectory Matching. J. Electr. Comput. Eng. 2018, 6296013 (2018). https://doi.org/10.1155/2018/6296013 [Google Scholar]
  16. M.H. Rahman, J. Afrin, Hand Gesture Recognition using Multiclass Support Vector Machine. Int. J. Comput. Appl. 74, 39–43 (2013). https://doi.org/10.5120/12852-9367 [Google Scholar]
  17. Arducam, OV7675 20-pin DVP Camera Module for Arduino, https://store.arduino.cc/products/arducam-camera-module (2024) [Google Scholar]
  18. Start ASL, Sign Language Alphabet | 6 Free Downloads to Learn it Fast, https://www.startasl.com/american-sign-language-alphabet/ (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.