A vertical-horizontal-intersections feature based method for identification of bharatanatyam double hand mudra images

Bharatanatyam is an Indian classical dance, which has to be studied under an expert. In villages, semi-urban areas and foreign countries the experts are scarce. In order to promote, popularize and make it self-pursuable, this Indian art requires technological leveraging. With this motivation, the goal of this work is to automate identification of mudras through Image processing. This paper presents a three stage methodology for identification of 24 double hand mudra images of Bharatanatyam dance. In the first stage, acquired images of Bharatanatyam mudras are preprocessed to obtain contours of mudras using canny edge detector. In the second stage, cell features are extracted that include number of vertical and horizontal intersections of grid lines with the contours of the mudras. In the third stage, a rule based classifier is developed to classify the given image into 24 classes of mudras. The proposed method is implemented using OpenCV with Microsoft visual C++ IDE. The proposed method finds many applications such as e-learning of mudras and proper postures leading to self-learning of Bharatanatyam dance, online commentary during concerts, and adoption to many other forms of dances prevailing in India and outside.