Abstract
Communication plays a very important role in human life. The ease of text-based communication in the form of emails and text-chats has increased nowadays due to the interaction of system and hardware devices. The research discussed in the present paper proposes to work upon a human–computer interaction system so that human and machine can communicate with each other without the actual use of any hardware input device like keyboards. Gesture keyboard is one such method by which we can achieve this goal of interacting with the computer using our hand gestures. The present research paper is a comparative study of seven machine earning classifiers aiming to increase the accuracy of prediction. The two main aims of this innovative research are to develop a gesture keyboard device which can be used to aid those people who have some kind of disability in vision so that they can use this device to interact with the computer and to increase the performance of the model by using methods like Bagging and Boosting.
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References
Choras RS, Kozik R (2017) Markerless head gesture recognition for human computer interaction. Recent Pat Signal Process
Dardas NH, Georganas ND (2011) Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques. IEEE Trans Instrum Meas 60(11):3592–3607
David PA (1985) Clio and the economics of QWERTY. Am Econ Rev 75:332–337
Breiman L (2000) Randomizing outputs to increase prediction accuracy. Mach Learn 40(3):229–242
Drucker H, Schapire R, Simard P (1993) Improving performance in neural networks using boosting algorithm. In: Hanson SJ, Cowan JD, Giles CL (eds) Advances in neural information processing systems, pp 42–49
Grif SH, Farcas CC (2016) Mouse cursor control system based on hand gesture. Proc Technol 22:657–661
Geurts; Lucas Jacobus Franciscus (Best, NL), Djajadiningrat; Johan Partomo (Utrecht, NL), De Bont; Jeanne (Eindhoven, NL), Chao; Pei-Yin (Eindhoven, NL), Gesture-based user-interface with user-feedback. http://patft.uspto.gov. Accessed 21 Mar 2013
Kramer KH (2009) Gestural control of autonomous and semi-autonomous systems. http://patft.uspto.gov. Accessed 10 Sept 2009
Kristensson PO (2009) Five challenges for intelligent text entry methods. AI Mag 30(4):85–94
Freeman WT, Roth M (1995) Orientation histograms for hand gesture recognition. International workshop on Automatic face and gesture recognition. 12:296–301
Juha K, Panu K, Jani M, Sanna K, Giuseppe S, Luca J, Sergio DM (2005) Accelerometer-based gesture control for a design environment. Springer, Finland
Lenman S, Bretzner L, Eiderbäck B (2002) Computer vision-based recognition of hand gestures for human-computer interaction. ISSN 1403 – 0721
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Gupta, R., Rana, S., Gupta, S., Pandey, K., Dabas, C. (2020). Comparative Analysis of Various Classifiers for Gesture Recognition. In: Kalam, A., Niazi, K., Soni, A., Siddiqui, S., Mundra, A. (eds) Intelligent Computing Techniques for Smart Energy Systems. Lecture Notes in Electrical Engineering, vol 607. Springer, Singapore. https://doi.org/10.1007/978-981-15-0214-9_11
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DOI: https://doi.org/10.1007/978-981-15-0214-9_11
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