Last week I presented our work on enhancing our $N multistroke recognizer by integrating Yang Li‘s Protractor matching method at GI 2012. The presentation was more algorithm-focused than most of the HCI talks in the conference (and most of my other work), but still generated a few interesting questions. The slides for the talk are posted here. Stay tuned for future enhancements to the $-family of recognizers!
Tag Archives: gi 2012
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.