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Desktop Handling Using ASL
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
During the corona virus outbreak in 2020, the need of contactless ways to carry out day-to-day task to help reduce spreading of infection was highlighted. There are traditional ways of Human-computer interaction which use physical devices like keyboard or mouse. This paper focuses on the achievement of effective contactless human?computer interaction using only webcam and hand signs to navigate computer screen and perform various task. American Sign Language is a universal sign language used by the people with Hearing and Speech Disabilities for communication in their daily activities. It is completely vision-based communication language which provides standard set of hand signs. In this research paper, presented an optimal approach for human- computer interaction which uses ASL signs detected using Machine Learning algorithm for mouse navigation.
Keywords
Dataset Generation, Feature extraction and Representation, CNN, Machine Learning, Keras, Human-Machine Interaction, GUI Application
References
[1] A. Pradhan and B. B. V. L. Deepak, "Obtaining hand gesture parameters using image processing,"2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Chennai, 2015, pp. 168-170, 2015.
[2] V. Bhame, R. Sreemathy and H. Dhumal, "Vision based hand gesture recognition using eccentric approach for human computer interaction,"2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), New Delhi, 2014, pp. 949-953,2014.
[3] V. Ranga, N. Yadav, and P. Garg, “American sign language fingerspelling using hybrid discrete wavelet transform-gabor filter and convolutional neural network,” Journal of Engineering Science and Technology, vol. 13, no. 9, pp. 2655–2669, 2018.
[4] Raimundo F. Pinto, Carlos D. B. Borges, Antônio M. A. Almeida, Iális C. Paula, "Static Hand Gesture Recognition Based on Convolutional Neural Networks", Journal of Electrical and Computer Engineering, vol. 2019, Article ID 4167890, 12 pages, 2019. https://doi.org/10.1155/2019/4167890
[5] Li, G., Tang, H., Sun, Y. et al. Hand gesture recognition based on convolution neural network. Cluster Comput 22, 2719–2729 (2019). https://doi.org/10.1007/s10586-017-1435-x
[6] Oyedotun, Oyebade & Khashman, Adnan. (2017). Deep learning in vision-based static hand gesture recognition. Neural Computing and Applications. 28. 10.1007/s00521-016-2294-8.
How to cite this paper
@article{1702694,
author = {Gitesh V. Sagvekar, Yuvraj G. Rane, Rohit R. Parab, Omkar D. Dike},
title = {Desktop Handling Using ASL},
journal = {Iconic Research And Engineering Journals},
year = {2021},
volume = {4},
number = {11},
pages = {43-48},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1702694.pdf},
abstract = {During the corona virus outbreak in 2020, the need of contactless ways to carry out day-to-day task to help reduce spreading of infection was highlighted. There are traditional ways of Human-computer interaction which use physical devices like keyboard or mouse. This paper focuses on the achievement of effective contactless human?computer interaction using only webcam and hand signs to navigate computer screen and perform various task. American Sign Language is a universal sign language used by the people with Hearing and Speech Disabilities for communication in their daily activities. It is completely vision-based communication language which provides standard set of hand signs. In this research paper, presented an optimal approach for human- computer interaction which uses ASL signs detected using Machine Learning algorithm for mouse navigation.},
keywords = {Dataset Generation, Feature extraction and Representation, CNN, Machine Learning, Keras, Human-Machine Interaction, GUI Application},
month = {May},
}