Hand Recognition and Motion Analysis using Faster RCNN
Traditionally wired keyboard and mouse were used as inputting devices to the computer system. These models had many drawbacks like hardware bulkiness and inefficiency due to delay. In this paper the new proposed system will input data using hand recognition and motion analysis. Webcam is used as the primary device to capture the hand gesture. These images are preprocessed to remove noise and for bit reduction. After the image preprocessing feature extraction and segmentation are done. CNN algorithms are used for object detection by identifying the features of the hand. For the system to identify the gesture, a number of images are used for training the system. This process is known as Features training. Once the features and the gestures are recognized, it is mapped to a particular function of the mouse and keyboard. � 2019 IEEE.