System application control based on Hand gesture using Deep learning
V Niranjani, R Keerthana, B Mohana Priya, K Nekalya, Anantha Krishnan Padmanabhan · 2021
In general, the Human Computer Interaction progresses toward interfaces that seem to be natural and intuitive to use rather than the customary usage of keyboard and mouse. Hand gesture recognition system is one of crucial techniques to build user-friendly interfaces, because of its diversified application and the potential of interacting with machines proficiently. Hand gestures including the movement of hands, fingers or arms are considerable for interaction. The proof levels of the hand gesture is perceived from the level of static gesture to the dynamic gestures or intricate foundation through which the communication of human feeling with computers succeed to occur. The proposed solution is framed by the identification of hand gestures as it possesses the perk of being used effortlessly and does not require an intervening medium. The existing system for the application access is inflexible and arduous for people with blindness and hand deformity regarding the human-computer interaction. A deep convolutional neural network (DCNN) is put forward in this paper, to use hand gestures recognition and immediately classify them by preserving even the not-hand area without any detection or segmentation process. Hence the proposed objective is to use different hand gestures via integrated webcam with the aid of deep learning concept being beneficial for the visually impaired and people with hand disability.