Sign To Text Conversion- Helping Aid

Authors

  • Vatsal Patel  Devang Patel Institute of Advance Technology and Research, Charusat University, Gujarat, India
  • Maahi Patel  Devang Patel Institute of Advance Technology and Research, Charusat University, Gujarat, India

DOI:

https://doi.org//10.32628/CSEIT217526

Keywords:

Sign recognition, CNN, Text conversion, American Sign Language

Abstract

The ancient way of sign language is most natural forms of communication. The recognition of sign is place a key role in research field. The development and improvement on this kind of work need more and more new techniques to analyze the accurate results. Many people don't know it and interpreters are hard to come by, we developed a real-time technique for finger spelling-based American Sign Language using neural networks. In our technique, the hand is first sent through a filter, and then it is passed through a classifier, which analyses the class of hand movements. For each alphabet the proposed model has a 96 percent accuracy rate. This model mainly implemented for Dumb and Deaf people for communication.

References

  1. NB, M. K. (2018). Conversion of sign language into text. International Journal of Applied Engineering Research, 13(9), 7154-7161.
  2. K. Tiku, J. Maloo, A. Ramesh and I. R., "Real-time Conversion of Sign Language to Text and Speech," 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), 2020, pp. 346-351, doi: 10.1109/ICIRCA48905.2020.9182877.
  3. Huang, J., Zhou, W., Li, H., & Li, W. (2015, June). Sign language recognition using 3d convolutional neural networks. In 2015 IEEE international conference on multimedia and expo (ICME) (pp. 1-6). IEEE.
  4. Karthick, P., Prathiba, N., Rekha, V. B., & Thanalaxmi, S. (2014). Transforming Indian sign language into text using leap motion. International Journal of Innovative Research in Science, Engineering and Technology, 3(4), 5.
  5. Anuja V.Nair, Bindu.V, “A Review on Indian Sign Language Recognition”, International journal of computer applications, Vol. 73, pp: 22, (2013).
  6. Tokuda, K.; Nankaku, Y.; Toda, T.; Zen, H.; Yamagishi, J.; Oura, K., “Speech Synthesis Based on Hidden Markov Models”, in Proceedings of the IEEE, vol.101, no.5, pp.1234-1252, (2013).
  7. Archana S. Ghotkar, Rucha Khatal, Sanjana Khupase, Surbhi Asati & Mithila Hadap, “Hand Gesture Recognition for Indian Sign Language”, IEEE International Conference on Computer Communication and Informatics (lCCCI ), pp: 1-4, (2012).
  8. Pigou L., Dieleman S., Kindermans PJ., Schrauwen B. (2015) Sign Language Recognition Using Convolutional Neural Networks. In: Agapito L., Bronstein M., Rother C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science, vol 8925. Springer, Cham
  9. Zaki, M.M., Shaheen, S.I.: Sign language recognition using a combination of new vision-based features. Pattern Recognition Letters 32(4), 572–577 (2011)
  10. N. Mukai, N. Harada and Y. Chang, "Japanese Fingerspelling Recognition Based on Classification Tree and Machine Learning," 2017 Nicograph International (NicoInt), Kyoto, Japan, 2017, pp. 19-24. doi:10.1109/NICOInt.2017.9
  11. Byeongkeun Kang, Subarna Tripathi, Truong Q. Nguyen” Real-time sign language fingerspelling recognition using convolutional neural networks from depth map” 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)
  12. Ruize Xu, Shengli Zhou, Li, W.J, “MEMS Accelerometer Based Nonspecific-User Hand Gesture Recognition”, IEEE Sensors Journal, vol.12, no.5, pp.1166-1173, (2012).
  13. Rajam, P. Subha and Dr G Bala krishnan, "Real Time Indian Sign Language Recognition System to aid Deaf and Dumb people", 13thInternational Conference on Communication Technology (ICCT), pp. 737-742, (2011).
  14. Prof. Rajeshri Rahul Itkarkar, “A Study of Vision Based Hand Gesture Recognition for Human Machine Interaction”, International Journal of Innovative Research in Advanced Engineering, Vol. 1, pp:12, (2014).
  15. Pravin R Futane, Rajiv V Dharaskar, “Hasta Mudra an interpretatoin of Indian sign hand gestures”, IEEE 3 rd International Conference on Electronics Computer Technology, Vol.2, pp:377-380, (2011).
  16. Meenakshi Panwar, “Hand Gesture Recognition based on Shape Parameters” International Conference on Computing, Communication and Application (ICCCA), pp: I-6, IEEE, (2012).
  17. Baker-Shenk, C. L., & Cokely, D. (1991). American Sign Language: A teacher's resource text on grammar and culture. Gallaudet University Press.
  18. Yegnanarayana, B. (2009). Artificial neural networks. PHI Learning Pvt. Ltd.
  19. Liu, T., Fang, S., Zhao, Y., Wang, P., & Zhang, J. (2015). Implementation of training convolutional neural networks. arXiv preprint arXiv:1506.01195.
  20. Bradski, G., & Kaehler, A. (2008). Learning OpenCV: Computer vision with the OpenCV library. " O'Reilly Media, Inc.".
  21. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... & Zheng, X. (2016). Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16) (pp. 265-283).

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2021-10-30

Issue

Volume 7, Issue 5, September-October-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Vatsal Patel, Maahi Patel, " Sign To Text Conversion- Helping Aid, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 5, pp.69-75, September-October-2021. Available at doi : https://doi.org/10.32628/CSEIT217526