Vision-Based Mid-Air Unistroke Character Input Using Polar Signatures

Abstract

Hand-gesture-based commands may replace touch and electromechanical input panels to make public interactive response systems (IRS) more accessible. This work presents a prototype framework for vision-based mid-air unistroke character input, which can be adapted as an interface for the IRS. At first, we developed an acquisition module that effectively spots the legitimate gesture trajectory by implementing pen-up and pen-down actions using depth thresholding and velocity tracking. The extracted trajectory is recognized through a novel, fast, and easy to implement the equipolar signature (EPS) technique. Apart from resistance to rotation, scale, and translation variations, EPS exhibits neutrality to stroking directions as well. On the three self-collected datasets comprising of digits, alphabets, and symbols, the EPS scheme obtains over 96.5% accurate results with an average of 30-ms running time. The proposed scheme is also validated on an open dataset DAIR (Dataset for AIR Handwriting), where it achieves 95.5% mean accuracy with 24.3-ms recognition time per gesture. The developed approach is compared with benchmark schemes to justify its accuracy and speed.