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Open Access
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Anjali Singh, Anjali Singh, Badal Singh, Deepa Shukla, Kirti Pandey, A.K. Dohare, M. S. Mandloi,
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- Student, Department of Electronics & Communication Engineering,, Student, Department of Electronics & Communication Engineering,, Student, Department of Electronics & Communication Engineering,, Student, Department of Electronics & Communication Engineering,, Student, Department of Electronics & Communication Engineering,, Professor, Department of Electronics & Communication Engineering,, Professor, Department of Electronics & Communication Engineering, Rewa Engineering College,, Rewa Engineering College,, Rewa Engineering College,, Rewa Engineering College,, Rewa Engineering College,, Rewa Engineering College,, Rewa Engineering College, Rewa,, Rewa,, Rewa,, Rewa,, Rewa,, Rewa,, Rewa, India, India, India, India, India, India, India
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Abstract
nSign 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.
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Keywords: American sign language (ASL), Sign language, hand gesture, convolutional neural network (CNN), machine learning, deep learning, OpenCv, Keras
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Electronic Design Technology(joedt)]
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References
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- Pala, J. B. Jethwani, S. S. Kumbhar and S. D. Patil, “Machine Learning-based Hand Sign Recognition,” 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Coimbatore, India, 2021, pp.356-363, doi:10.1109/ICAIS50930.2021.9396030.
- Lionel Pigou, Sander Dieleman, Pieter- Jan Kindermans and Benjamin Schrauwen, “Sign Language Recognition Using Convolutional Neural Networks,” 2015 European Conference on Computer Vision.
- K. Bhagat, Y. Vishnusai and G. N. Rathna, “Indian Sign Language Gesture Recognition using Image Processing and Deep Learning,” 2019 Digital Image Computing: Techniques and Applications (DICTA), Perth, WA, Australia, 2019, pp. 1- 8, doi: 10.1109/DICTA47822.2019.8945850
- Dutta KK, Bellary SA. Machine learning techniques for Indian sign language recognition. In2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC) 2017 Sep 8 (pp. 333-336). IEEE.
- Patil R, Patil V, Bahuguna A, Datkhile G. Indian sign language recognition using convolutional neural network. InITM web of conferences 2021 (Vol. 40, p. 03004). EDP Sciences.
- Bantupalli and Y. Xie, “American Sign Language Recognition using Deep Learning and Computer Vision,” 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018, pp. 4896- 4899, doi: 10.1109/BigData.2018.8622141.
- 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.
- Liu, W. Zhou and H. Li, “Sign language recognition with long short-term memory,” 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 2016, pp. 2871-2875, doi: 10.1109/ICIP.2016.7532884.
- Zhang, W. Zhou, C. Xie, J. Pu and H. Li, “Chinese sign language recognition with adaptive HMM,” 2016 IEEE International Conference on Multimedia and Expo (ICME), Seattle, WA, USA, 2016, pp. 1-6, doi: 10.1109/ICME.2016.7552950.
- P. C. Badhe and V. Kulkarni, “Indian sign language translator using gesture recognition algorithm,” 2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS), Bhubaneswar, India, 2015, pp. 195-200, doi: 10.1109/CGVIS.2015.7449921
- Adhikari, M. S. Bin Jahangir, I. Jahan, M. S. Mia and M. R. Hassan, “A Sign Language Recognition System for Helping Disabled People,” 2023 5th International Conference on Sustainable Technologies for Industry 5.0 (STI), Dhaka, Bangladesh, 2023, pp.1-6,doi: 10.1109/STI59863.2023.10465011.
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Volume | 15 | |
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
Received | July 16, 2024 | |
Accepted | July 24, 2024 | |
Published | August 17, 2024 |
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