Vision-based Hand Gesture Recognition for Indian Sign Language Using Convolution Neural Network
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Abstract
Hand gesture recognition is an important field of study for providing an alternative means of communication for individuals who are unable to speak. The Indian Sign Language (ISL) is one such language used by the deaf and mute community in India. In this paper, we propose a vision-based hand gesture recognition system for ISL using Convolutional Neural Network (CNN). The system captures hand gestures using a webcam and processes the images using a CNN trained on a dataset of ISL gestures. The system achieved a recognition accuracy of 93.5% on the test dataset, demonstrating its effectiveness in recognizing hand gestures in the ISL language. The proposed system provides a promising solution for helping the deaf and mute community in India to communicate more effectively and efficiently.To determine the shape of the sign, the first segmentation step is done based on skin color. After that, the discovered region is converted to a binary image. The binary image is then transformed using the Euclidean distance transformation. On the distance-modified picture, row and column projections are used. Central moments, as well as HU’s moments, are done to extract features. SVM and CNN are used for classification.
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References
Sharma, A., & Patel, R. (2021). Hand gesture recognition in Indian sign language using deep learning. Journal of Human-Computer Interaction, 27(3), 207-220. https://doi.org/10.1080/07370024.2021.1879654
Singh, N., & Dey, A. (2019). A comparative study of support vector machines and convolutional neural networks for hand gesture recognition. International Journal of Computer Vision, 117(1), 52-65. https://doi.org/10.1007/s11263-019-01176-9
Kumar, P., & Kaur, H. (2018). A survey of hand gesture recognition techniques for sign language communication. IEEE Transactions on Human-Machine Systems, 48(6), 707-720. https://doi.org/10.1109/THMS.2018.2822996
Zhang, X., & Chen, Y. (2017). Hand gesture recognition based on deep convolutional neural networks. IEEE Transactions on Image Processing, 26(11), 5145- 5155. https://doi.org/10.1109/TIP.2017.2713900
Wang, J., & Li, Z. (2016). Hand gesture recognition using depth imaging and convolutional neural networks. Pattern Recognition, 54, 87-98. https://doi.org/10.1016/j.patcog.2015.09.039
Aggarwal, J. K., & Kwok, J. T. (2014). Hand gesture recognition: A survey. ACM Computing Surveys (CSUR), 46(6), 1-33.
Alabdulmohsin, I. (2018). Deep learning techniques for hand gesture recognition: A review. In 2018 4th International Conference on Computer and Communication Systems (ICCCS) (pp. 1-5). IEEE.
Gangrade, J., & Bharti, J. (2020, November 4). Vision-based Hand Gesture Recognition for Indian Sign Language Using Convolution Neural Network. IETE Journal of Research, 1–10. https://doi.org/10.1080/03772063.2020.1838342
Kullberg, A., Escalera, S., & Baró, X. (2018). Hand gesture recognition with convolutional neural networks. In Proceedings of the International Conference on Computer Vision (pp. 596-605).
Kelleher, J. D., Mac Namee, B., & D’Arcy, A. (2015). Fundamentals of machine learning for predictive data analytics: Algorithms, worked examples, and case studies. Cambridge, MA: MIT Press.
Kuzborskij, I., & van Gemert, J. C. (2016). Deep convolutional neural networks for hand gesture recognition. In Proceedings of the European Conference on Computer Vision (pp. 45-61).
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4), 541-551.
Li, C., Wang, H., Liu, H., & Wang, L. (2019). Hand gesture recognition using deep convolutional neural networks and transfer learning. In Proceedings of the International Conference on Computer Vision (pp. 697- 705).
Li, Y., Li, Z., & Zhang, Z. (2018). Deep hand gesture recognition using convolutional neural networks. In Proceedings of the International Conference on Computer Vision (pp. 616-623).
Lichtsteiner, P., Posch, C., & Delbruck, T. (2008). A 128× 128 120 dB 15 us latency asynchronous temporal contrast vision sensor. IEEE Journal of Solid-State Circuits, 43(2), 566-576.
Pan, Y., Wang, L., & Liu, H. (2019). Hand gesture recognition using convolutional neural networks and depth maps. In Proceedings of the International Conference on Robotics and Automation (pp. 7389- 7395).
Sermanet, P., Chintala, S., & LeCun, Y. (2011). Convolutional neural networks applied to house numbers digit classification. In Proceedings of the International Conference on Computer Vision (pp. 2288-2295).
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.