$1 Unistroke Recognizer
Jacob O. Wobbrock, University of Washington [contact]
Andrew D. Wilson, Microsoft Research
Yang Li, University of Washington†
†Currently at Google
The $1 Unistroke Recognizer is a 2-D single-stroke recognizer designed for rapid prototyping of gesture-based user interfaces. In machine learning terms, $1 is an instance-based nearest-neighbor classifier with a Euclidean scoring function, i.e., a geometric template matcher. $1 is an extension of the proportional shape matching approach used in SHARK2, which itself is an adaptation of Tappert's elastic matching approach with zero look-ahead. Despite its simplicity, $1 requires very few templates to perform well and is only about 100 lines of code, making it easy to deploy. An optional enhancement called Protractor improves $1's speed. The $N Multistroke Recognizer extends $1 to gestures with multiple strokes. The $P Point-Cloud Recognizer is the latest in the dollar family, performing unistroke and multistroke recognition without the combinatoric overhead of $N. The $1 recognizer is distributed under the New BSD License agreement.
In the demo below, only one unistroke template is loaded for each of the 16 gesture types. You can add additional unistrokes as you wish, and even define your own custom unistrokes.
Make strokes on this canvas. If a misrecognition occurs, add the misrecognized unistroke as an example of the intended gesture.
Wobbrock, J.O., Wilson, A.D. and Li, Y. (2007). Gestures without libraries, toolkits or training: A $1 recognizer for user interface prototypes. Proceedings of the ACM Symposium on User Interface Software and Technology (UIST '07). Newport, Rhode Island (October 7-10, 2007). New York: ACM Press, pp. 159-168.
Li, Y. (2010). Protractor: A fast and accurate gesture recognizer. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '10). Atlanta, Georgia (April 10-15, 2010). New York: ACM Press, pp. 2169-2172.
Kristensson, P. and Zhai, S. (2004). SHARK2: A large vocabulary shorthand writing system for pen-based computers. Proceedings of the ACM Symposium on User Interface Software and Technology (UIST '04). Santa Fe, New Mexico (October 24-27, 2004). New York: ACM Press, pp. 43-52.
Tappert, C.C. (1982). Cursive script recognition by elastic matching. IBM Journal of Research and Development 26 (6), pp. 765-771.
$1 By Others
Copyright © 2007-2012 Jacob O. Wobbrock