International Journal of Scientific & Technology Research

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IJSTR >> Volume 9 - Issue 4, April 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Human Computer Interaction Through Hand Gesture Recognition Technology

[Full Text]



Nikhat Parveen, Arpita Roy, D. Sai Sandesh, J.Y.P.R.Sai Srinivasulu, N.Srikanth



(deaf, dumb, gestures, HGR, SVM, HCI, SIFT, HOG)



Communication is the main channel to interact between individuals with each other. Due to birth defects, accidents and oral disorders, there has been a drastic increase in the number of deaf and dumb victims in recent years. Since deaf and dumb people are unable to communicate with ordinary people, they must rely on some kind of visual communication. Throughout the world, most languages are spoken and interpreted. The people, that is, those who find it difficult to speak and hear "The Dumb" and "The Deaf," It's hard to understand exactly what the other person is trying to communicate with the deaf, and so on. Key recognition issues for hand gesture are consistent with the complexities of the gesture process. Also offered methods for evaluating the process of identifying recent postures and gestures. Hand gesture recognition is becoming an increasingly popular field of research in human computer interaction. Considering the similarity of human hand form with four fingers and a thumb, this paper aims to present a real-time hand gesture recognition system focusing on recognizing some important shape-based features such as orientation, center of mass (centroid), finger position, thumb in terms of raised or folded hand fingers and their respective location in the image. The solution in this paper depends entirely on the shape parameters of the hand gesture. This does not include any other means of identification of hand gesture such as skin color, texture, as these image-based characteristics vary greatly from different light conditions to other variables. This basic form-based approach to hand gesture recognition proposed in this paper can identify around 1-5 different gestures based on the performance of this algorithm. This proposed implemented algorithm was evaluated over 50-60 images and provides approximately 94 percent recognition level. However, the recognition rate remains to be improved at the identification level.



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