Highly Accurate Trimesh and PointNet based algorithm for Gesture and Hindi air writing recognition

Abstract

Air writing and gesture recognition has been one of the most comprehensive and intensively researched areas due to their wide variety of applications in several domains of Human-Computer Interaction (HCI),such as gaming, medicines, and automobiles. This article presents ahighly accurate point-based algorithm for identifying dynamic air writing and free hand gesture recognition for Devanagari Hindi characters.Usually, the written gestures are limited in nature. In order to create a training data set, we are using Leap Motion Controller (LMC) that generates twenty-four sample points from the gesture drawn. In the pre-processing stage, a trimesh is generated using these sample points; the developed trimesh is then fed as input to PointNet. The algorithm developed measures the similarity of the drawn gesture with the training samples to identify and recognize the gesture drawn using Mahalanobis distance. The validation test indicates that the approach is quite accurate, giving a recognition rate of more than 97%. Comparative studies also show that the methodology performs better when trimesh is used as input for the PointNet and more training samples are used.