Proceedings: GI 2012

$N-protractor: a fast and accurate multistroke recognizer

Lisa Anthony , Jacob Wobbrock

Proceedings of Graphics Interface 2012: Toronto, Ontario, Canada, 28 - 30 May 2012, 117-120

DOI 10.20380/GI2012.15

  • Bibtex

    @inproceedings{Anthony:2012:10.20380/GI2012.15,
    author = {Anthony, Lisa and Wobbrock, Jacob},
    title = {$N-protractor: a fast and accurate multistroke recognizer},
    booktitle = {Proceedings of Graphics Interface 2012},
    series = {GI 2012},
    year = {2012},
    issn = {0713-5424},
    isbn = {978-1-4503-1420-6},
    location = {Toronto, Ontario, Canada},
    pages = {117--120},
    numpages = {4},
    doi = {10.20380/GI2012.15},
    publisher = {Canadian Human-Computer Communications Society},
    address = {Toronto, Ontario, Canada},
    }

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.