| Issue |
E3S Web Conf.
Volume 391, 2023
4th International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2023)
|
|
|---|---|---|
| Article Number | 01060 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.1051/e3sconf/202339101060 | |
| Published online | 05 June 2023 | |
Hand gesture recognition and voice conversion for deaf and Dumb
1 Assisstance Professor, Department of ECE, Gokaraju Rangaraju Institute of Engineering and Technology, India
2 Department of ECE, Gokaraju Rangaraju Institute of Engineering and Technology, India
3 Department of ECE, Gokaraju Rangaraju Institute of Engineering and Technology, India
4 Department of ECE, Gokaraju Rangaraju Institute of Engineering and Technology, India
* Corresponding Author: biradarshivani098@gmail.com
In this paper, we purpose a Hand gesture recognition model which can be used in real time application. This model is based on the mediapipe frame work of the google, Tensor flow in openCv and python and classification using feed forward neural network with keras model. The structure of the proposed work consists of 3 modules: Grabbing the frames, detecting hand landmarks and classification. The proposed model has the accuracy 95.7% at recognizing 10 kinds of hand gestures(Thumbs up, Thumbs down, Peace, Smile, Rock, Ok, Fist, livelong, call me, stop). A hand gesture recognition model that reacts rapidly and with generally acceptable accuracy is one of this work’s primary achievements and pre trained model for feature extraction. The unique approach of the suggested approach is that it detects hand landmarks using Google’s MediaPipe, which is faster and more accurate than traditional methods that rely on geometry, form, and edge data. For modelling sequence data and for recognising gestures, the LSTM model has proven to be quite successful.
Key words: hand gesture recognition / realtime / feed forward neural networks / mediapipe / Tensor flow / opencv / keras / python
© The Authors, published by EDP Sciences, 2023
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