Anjali Singh,
Anjali Singh,
Badal Singh,
Deepa Shukla,
Kirti Pandey,
A.K. Dohare,
M. S. Mandloi,
- Student, Department of Electronics & Communication Engineering, Rewa Engineering College, Rewa, India
- Student, Department of Electronics & Communication Engineering, Rewa Engineering College, Rewa, India
- Student, Department of Electronics & Communication Engineering, Rewa Engineering College, Rewa, India
- Student, Department of Electronics & Communication Engineering, Rewa Engineering College, Rewa, India
- Student, Department of Electronics & Communication Engineering, Rewa Engineering College, Rewa, India
- Professor, Department of Electronics & Communication Engineering, Rewa Engineering College, Rewa, India
- Professor, Department of Electronics & Communication Engineering, Rewa Engineering College, Rewa, India
Abstract
Sign language detection plays a pivotal role in bridging communication barriers for the deaf and hard of hearing community. An extensive investigation on the use of convolutional neural networks (CNNs) for sign language recognition is presented in this article. Leveraging the power of deep learning, our research aims to develop an accurate and efficient system capable of recognizing and classifying sign language gestures in real-time. The report begins with an overview of the significance of sign language detection in fostering inclusivity and accessibility. The approach is then covered in detail, including the architecture of the CNN model that was used, methods for gathering datasets and preparing them, and the training procedure. Results obtained from experimentation are presented, showcasing the effectiveness of the CNN model in accurately detecting a diverse range of sign language gestures. In conclusion, this report highlights the promising capabilities of CNN-based sign language detection systems, emphasizing their potential to enhance accessibility and communication for the deaf and hard of hearing community.
Keywords: American sign language (ASL), Sign language, hand gesture, convolutional neural network (CNN), machine learning, deep learning, OpenCv, Keras
[This article belongs to Journal of Electronic Design Technology(joedt)]
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References
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Journal of Electronic Design Technology
| Volume | 15 |
| Issue | 02 |
| Received | July 16, 2024 |
| Accepted | July 24, 2024 |
| Published | August 17, 2024 |