An Efficient Framework for 2-Dimensional Gesture Based Telugu Character Recognition

Gesture identification plays a vital role in today's human-computer interaction. In this paper, we proposed a sensor based gesture recognition system which makes the teacher to write in Telugu language on digital board from anywhere within the class room. Various classification algorithms k-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Decision tree are individually used for hand gesture based Telugu character recognition. Here, we assess the performance of three classification algorithms which are compared with 16 different Telugu character vowel gestures. Each gesture is collected by using an inertial sensor based embedded device. The dataset contains 16 gestures, each gesture repeated for eleven times from three different people. The gesture identification accuracy for k-Nearest Neighbor classification is 97.2%, SVM is 92.8% and Decision tree is 86.5%.