Affiliation:
1. School of Computer Science & Engineering Bennett University (Times Group) Greater Noida Uttar Pradesh India
2. Department of Mathematics Jaypee Institute of Information Technology Noida Uttar Pradesh India
Abstract
AbstractAir‐writing recognition is relevant in areas such as natural human–computer interaction, augmented reality, and virtual reality. A trajectory is the most natural way to represent air writing. We analyze the recognition accuracy of words written in air considering five features, namely, writing direction, curvature, trajectory, orthocenter, and ellipsoid, as well as different parameters of a hidden Markov model classifier. Experiments were performed on two representative datasets, whose sample trajectories were collected using a Leap Motion Controller from a fingertip performing air writing. Dataset contains 840 English words from 21 classes, and dataset contains 1600 English words from 40 classes. A genetic algorithm was combined with a hidden Markov model classifier to obtain the best subset of features. Combination trajectory, orthocenter, writing direction, curvature provided the best feature set, achieving recognition accuracies on datasets and of 98.81% and 83.58%, respectively.
Subject
Electrical and Electronic Engineering,General Computer Science,Electronic, Optical and Magnetic Materials