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A real-time Artificial Intelligence system for learning Sign Language (2404.07211v1)

Published 19 Feb 2024 in cs.CV and cs.AI

Abstract: A primary challenge for the deaf and hearing-impaired community stems from the communication gap with the hearing society, which can greatly impact their daily lives and result in social exclusion. To foster inclusivity in society, our endeavor focuses on developing a cost-effective, resource-efficient, and open technology based on Artificial Intelligence, designed to assist people in learning and using Sign Language for communication. The analysis presented in this research paper intends to enrich the recent academic scientific literature on Sign Language solutions based on Artificial Intelligence, with a particular focus on American Sign Language (ASL). This research has yielded promising preliminary results and serves as a basis for further development.

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References (26)
  1. How many people use asl in the united states? why estimates need updating. Sign Language Studies, 6(3):306–335, 2006.
  2. Isolated asl sign recognition system for deaf persons. IEEE Transactions on rehabilitation engineering, 3(3):261–271, 1995.
  3. Mohammed Waleed Kadous et al. Machine recognition of auslan signs using powergloves: Towards large-lexicon recognition of sign language. In Proceedings of the Workshop on the Integration of Gesture in Language and Speech, volume 165, pages 165–174. DE Wilmington, 1996.
  4. Adapting hidden markov models for asl recognition by using three-dimensional computer vision methods. In 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, volume 1, pages 156–161. IEEE, 1997.
  5. Data gloves for sign language recognition system. International Journal of Computer Applications, 975:8887, 2015.
  6. M Kute and G Chinchole. Sign language to speech conversion device. In Proc. ICRTIT, pages 1–6, 2020.
  7. A position and rotation invariant framework for sign language recognition (slr) using kinect. Multimedia Tools and Applications, 77:8823–8846, 2018.
  8. Real-time american sign language from video using hidden markov models. MBR97, page, 1997.
  9. Sign language recognition using convolutional neural networks. In Computer Vision-ECCV 2014 Workshops: Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part I 13, pages 572–578. Springer, 2015.
  10. MM Kamruzzaman et al. Arabic sign language recognition and generating arabic speech using convolutional neural network. Wireless Communications and Mobile Computing, 2020, 2020.
  11. Sign language recognition: A deep survey. Expert Systems with Applications, 164:113794, 2021.
  12. Deep learning approach for sign language recognition. JITEKI: Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, 9(1), 2023.
  13. A hierarchical approach for spanish sign language recognition: From weak classification to robust recognition system. In Proceedings of SAI Intelligent Systems Conference, pages 37–53. Springer, 2022.
  14. Sign4all: A low-cost application for deaf people communication. IEEE Access, 2023.
  15. Colours and numerals in spanish sign language (lse). Semantic fields in sign languages: Colour, kinship and quantification. Sign language typology series, (6):73–122, 2016.
  16. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
  17. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9, 2015.
  18. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826, 2016.
  19. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
  20. François Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251–1258, 2017.
  21. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708, 2017.
  22. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017.
  23. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4510–4520, 2018.
  24. Searching for mobilenetv3. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1314–1324, 2019.
  25. Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8697–8710, 2018.
  26. Designing network design spaces. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10428–10436, 2020.

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