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Abstract

Instead of spoken words, sign languages use the visual-manual modality to communicate meaning. Different gestures, behaviours, and signals are used by people to communicate with one another. Nearly 20% of the world’s population, or more than 1.5 billion people, suffer from hearing loss, according to the World Health Organization. In today’s world, it can be quite difficult to communicate with those who are hard of hearing because their primary form of communication, sign language, always calls for an interpreter. Like hearing persons, the deaf community requires access to all information. The lack of any sign language corpus has prevented the development of many text-to-sign language conversion systems, despite a variety of tools being available to interpret or recognise sign language and convert it to text. Daily news is that aspect of communication that informs us of the evolving events, problems, and people in the outside world. People with hearing impairments should be able to comprehend news just like everyone else. Our study intends to develop a system in which any news article is scraped, summarised using natural language processing technique, and then translated into American sign language (ASL).

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References

  1. Alharbi R, Alhaisoni E, Alsolami F (2021) Sign language recognition using deep learning techniques: a review. J Ambient Intell Humanized Comput 12(5):4739–4760

    Google Scholar 

  2. Thanh Hoa P, Thang TQ, Anh PT, Vinh PT (2021) Sign language recognition system using MobileNet and LSTM. In: Proceedings of the 2021 5th international conference on innovation in artificial intelligence, pp 116–122

    Google Scholar 

  3. Onwukwe CE, Daramola JO (2021) Real-time sign language recognition using convolutional neural network. In: Proceedings of the 2021 IEEE 11th annual computing and communication workshop and conference, pp 001–006

    Google Scholar 

  4. Khan A, Ngadi MA (2021) Real-time hand gesture recognition for sign language interpretation using deep learning. In: Proceedings of the 2021 IEEE 17th international colloquium on signal processing & its applications, pp 105–109

    Google Scholar 

  5. Yang W, Cheng J (2021) Efficient sign language recognition using key frames selection and adaptive data augmentation. In: Proceedings of the 2021 IEEE international conference on multimedia and expo, pp 1–6

    Google Scholar 

  6. Minocha A, Bansal S, Sharma S, Mehra M (2021) Sign language recognition using faster R-CNN and spatial-temporal LSTM. In: Proceedings of the 2021 3rd international conference on emerging technologies in computer engineering: machine learning and internet of things, pp 53–58

    Google Scholar 

  7. Mandal P, Sankar S, Ravi S, Kumar P (2021) Deep learning based sign language recognition: a comparative study. In: Proceedings of the 2021 5th international conference on computing methodologies and communication, pp 111–119

    Google Scholar 

  8. Chen Z, Li W, Li W Wu L (2021) Sign language recognition based on spatial-temporal network and attention mechanism. In: Proceedings of the 2021 international conference on artificial intelligence and big data, pp 1–6

    Google Scholar 

  9. Alghamdi MM, Alghamdi M (n.d.) Hand gestures recognition using deep learning techniques for American Sign Language

    Google Scholar 

  10. Duggan D (2019) American Sign Language recognition using convolutional neural networks. J Electr Imaging 28:011012

    Google Scholar 

  11. Dubey A, Singh G (2021) Real-time Indian sign language recognition using CNN and LSTM networks. J Ambient Intell Human Comput 12:189–198

    Google Scholar 

  12. Wang Q, Yang Y, Li H (2021) A sign language recognition system based on multi-feature fusion and attention mechanism. IEEE Access 9:49633–49642

    Google Scholar 

  13. Li Y, Li Z, Hu J, Liu Y, Zhang D (2021) Sign language recognition system based on multi-features extraction and multi-task learning. In: 2021 IEEE international conference on artificial intelligence and computer applications (ICAICA), pp 326–332

    Google Scholar 

  14. Perera PD, Dissanayaka AT, Jayawardena N (2021) Sinhala sign language recognition using deep learning. In: 2021 7th international conference on information and communication technology for embedded systems (IC-ICTES), pp 1–6

    Google Scholar 

  15. Mao Y, Huang L, Qiao Y (2021) A deep learning model for American sign language recognition based on skeleton joint position. J Intell Syst 30(1):123–135

    Google Scholar 

  16. Azzaz HM, Ezzat MH (2021) Sign language recognition using a convolutional neural network trained by lifting wavelet transform. Wirel Personal Commun 118:2565–2583

    Google Scholar 

  17. Nasir SI, Ahmad N (2020) Sign language recognition using deep learning: a review. IEEE Access 8:191823–191843

    Google Scholar 

  18. Wang Z, Sun H, Liu M, Wu X (2021) Sign language recognition using video-to-video translation and deep learning. IEEE Access 9:64420–64429

    Google Scholar 

  19. Alfaifi M, Al-Nafjan A, Al-Dossari H (2021) Real-time sign language recognition using convolutional neural network. Sensors 21(4):1188

    Google Scholar 

  20. Tiwari A, Nema RK (2021) Sign language recognition using deep convolutional neural network with transfer learning approach. Wirel Personal Commun 118:3479–3494

    Google Scholar 

  21. Peng M, Yin C (2021) A hybrid deep learning network for sign language recognition based on semantic feature extraction. Int J Adv Rob Syst 18(1):1729881421989426

    Google Scholar 

  22. Abdallah IB, Amara Essoukri Ben N, Ben Mabrouk H (2021) Sign language recognition based on deep convolutional neural network with attention mechanism. Wirel Personal Commun 118:5243–5260

    Google Scholar 

  23. Panwar S, Patel S (2021) Sign language recognition using convolutional neural networks with data augmentation techniques. J King Saud Univ-Comput Inf Sci

    Google Scholar 

  24. Patel AK, Swain AK (2021) A comparative study on sign language recognition techniques using deep learning approaches. In: Advances in intelligent systems and computing, pp 267-274. Springer

    Google Scholar 

  25. Ahmed R, Albakri SH (2021) A comprehensive survey on sign language recognition using deep learning techniques. IEEE Access 9:25004–25028

    Google Scholar 

Download references

Acknowledgements

We would like to express our sincere gratitude to everyone who supported us throughout this research project. Firstly, we extend our heartfelt thanks to our mentor, who provided us with invaluable guidance, feedback, and support throughout the entire research process. We are grateful for their encouragement, insightful comments, and constructive criticism, which helped us to improve our work significantly.

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Correspondence to Isha Gawde .

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Gawde, I., Philip, J., Kanabar, K., Tholar, S., Chopra, S. (2023). SANKET—A Vision Beyond Gestures. In: Yadav, A., Nanda, S.J., Lim, MH. (eds) Proceedings of International Conference on Paradigms of Communication, Computing and Data Analytics. PCCDA 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-4626-6_56

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