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Real Time Hand Gesture Recognition in Depth Image using CNN

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Dardina Tasmere, Boshir Ahmed, Sanchita Rani Das

Dardina Tasmere, Boshir Ahmed and Sanchita Rani Das. Real Time Hand Gesture Recognition in Depth Image using CNN. International Journal of Computer Applications 174(16):28-32, January 2021. BibTeX

	author = {Dardina Tasmere and Boshir Ahmed and Sanchita Rani Das},
	title = {Real Time Hand Gesture Recognition in Depth Image using CNN},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2021},
	volume = {174},
	number = {16},
	month = {Jan},
	year = {2021},
	issn = {0975-8887},
	pages = {28-32},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2021921040},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Hand gestures can play a notable role in computer vision, and hand gesture-based methods can stand out in providing a native way of interaction. Deafness is a degree of loss such that a person is unable to understand speech, spoken language. Sign language declined the gap in spoken language. The hand gesture is analyzed identically to sign language presenting the naturalness of intercommunication for deaf people. Real-time hand gesture recognition has been proposed in our research. Our proposed CNN model architecture will remediate the communication barrier of deaf people. The proposed model has achieved an accuracy of 94.61% to recognize 11 several different gestures using depth images.


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Depth Image, Deep Convolution Neural Network, Real Time Recognition