My colleague Jacob O. Wobbrock and I have had a paper accepted to the Graphics Interface 2012 conference! We extend our $N multistroke recognizer to use the closed-form matching method of Yang Li‘s Protractor, speeding up the matching process significantly. The paper is titled “$N-Protractor: A Fast and Accurate Multistroke Recognizer,” and here is the abstract:
Prior work introduced $N, a simple multistroke gesture recognizer based on template matching, intended to be easy to port to new platforms for rapid prototyping, and derived from the unistroke $1 recognizer. $N uses an iterative search method to find the optimal angular alignment between two gesture templates, like $1 before it. Since then, Protractor has been introduced, a unistroke pen and finger gesture recognition algorithm also based on template-matching and $1, but using a closed-form template-matching method instead of an iterative search method, considerably improving recognition speed over $1. This paper presents work to streamline $N with Protractor by using Protractor’s closed-form matching approach, and demonstrates that similar speed benefits occur for multistroke gestures from datasets from multiple domains. We find that the Protractor enhancements are over 91% faster than the original $N, and negligibly less accurate (<0.2%). We also discuss the impact that the number of templates, the input speed, and input method (e.g., pen vs. finger) have on recognition accuracy, and examine the most confusable gestures.
Check out the camera ready version here. For the pseudocode of this method, see the $N project website.