Comparative Analysis of Various Classifiers for Gesture Recognition

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.