Real-Time Hand Gesture Calculator Using Convolution Neural Network (original) (raw)

Abstract

In recent years, the use of hand gestures has become increasingly significant in facilitating seamless human-computer interaction. In this sense, gesture recognition is quite important in our day-to-day lives. We are developing a hand gesture-based calculator that will help dyslexia persons or people having difficulties to comprehend mathematical concepts more readily. There are several applications for gesture recognition, including those in virtual reality, robotics, computer gaming, and sign language. This demonstrates how scientists’ primary research focus should be gesture recognition. We developed a quick and easy method of simple calculation using hand gestures. We created a custom dataset and built a Convolution Neural Network for the training purpose, compared to the existing system we were able to achieve a training accuracy percentage of 98.13%. Our system first identifies the hand segments in the live video feed, then recognizes the gesture using a pre-trained custom dataset, and then assigns numbers in the live feed. The goal of the study is to achieve human-computer connection that is as natural as human-human contact.

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References

  1. Mujahid, A., et al.: Real-Time Hand Gesture Recognition Based on Deep Learning YOLOv3 Model. Appl. Sci. 11, 4164 (2021). https://doi.org/10.3390/app11094164
    Article CAS Google Scholar
  2. Mohit, T., Prashant, C., Gita, R.: Hand gesture recognition based calculator. Int. J. Comp. Sci. Mobile Comput. 9 (2019). https://doi.org/10.1324/s242442-123-423
  3. Popov, P.A., Laganière, R.: Long Hands gesture recognition system: 2 step gesture recognition with machine learning and geometric shape analysis. Multimed Tools Appl 81, 40311–40342 (2022). https://doi.org/10.1007/s11042-022-12870-8
    Article Google Scholar
  4. Sahoo, J.P., Allam, J.P., Paweł, P., Saunak, S.: Real-time hand gesture recognition using fine-tuned convolutional neural network. Sensors 22(3), 706 (2022). https://doi.org/10.3390/s22030706
  5. Wang, X., Jiang, J., Wei, Y., Kang, L., Gao, Y.: Research on gesture recognition method based on computer vision. MATEC Web of Conferences 232, 03042 (2018). https://doi.org/10.1051/matecconf/201823203042
    Article Google Scholar
  6. Murthy, G.R.S., Jadon, R.S.: A review of vision based hand gestures recognition. Int. J. Info. Technol. Knowl. Manage. 2(2), 405–410 (2009)
    Google Scholar
  7. Garg, P., Aggarwal, N., Sofat, S.: Vision based hand gesture recognition, world academy of science. Eng. Technol. 49, 972–977 (2009)
    Google Scholar
  8. Chu, X., Liu, J., Shimamoto, S.: A sensor-based hand gesture recognition system for japanese sign language. In: 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech), pp. 311–312. Nara, Japan (2021). https://doi.org/10.1109/LifeTech52111.2021.9391981
  9. Reddy, V.V., Dhyanchand, T., Krishna, G.V., Maheshwaram, S.: Virtual mouse control using colored finger tips and hand gesture recognition. 2020 IEEE-HYDCON, pp. 1–5. Hyderabad, India (2020). https://doi.org/10.1109/HYDCON48903.2020.9242677
  10. Wang, X., Veeramani, D., Zhu, Z.: Gaze-aware hand gesture recognition for intelligent construction. Eng. Appl. Artif. Intell. 123, 106179 (2023). https://doi.org/10.1016/j.engappai.2023.106179
    Article Google Scholar

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Authors and Affiliations

  1. Hindustan Institute of Technology and Science, Chennai, 603103, India
    Syam Chandrasekharan, K. Anand & Praisy Evangelin

Authors

  1. Syam Chandrasekharan
  2. K. Anand
  3. Praisy Evangelin

Corresponding author

Correspondence toSyam Chandrasekharan .

Editor information

Editors and Affiliations

  1. SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
    Annie Uthra R.
  2. Department of Computer Technology, Anna University, Chennai, Tamil Nadu, India
    Kottilingam Kottursamy
  3. Department of Computer Technology, Anna University, Chennai, Tamil Nadu, India
    Gunasekaran Raja
  4. Manchester Metropolitan University, Manchester, UK
    Ali Kashif Bashir
  5. Department of Computer Engineering, Süleyman Demirel University, Isparta, Türkiye
    Utku Kose
  6. SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
    Revathi Appavoo
  7. SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
    Vimaladevi Madhivanan

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Chandrasekharan, S., Anand, K., Evangelin, P. (2024). Real-Time Hand Gesture Calculator Using Convolution Neural Network. In: R., A.U., et al. Deep Sciences for Computing and Communications. IconDeepCom 2023. Communications in Computer and Information Science, vol 2176. Springer, Cham. https://doi.org/10.1007/978-3-031-68905-5\_42

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