Reading #8: Lightweight Multistroke Recognizer for UI Prototypes

Comments:
Yue Li

Summary:
When the $1 Recognizer grew up, it evolved into the $N Recognizer. $N is a lightweight, multistroke recognizer that can provide increased accuracy and decreased time via the addition of optional optimizers. Again, the focus is on providing designers with an easy to implement and use recognition system for augmenting their software and designs. Here are the changes from $1:

1. A novel way to represent a multistroke as a set of unistrokes representing all possible stroke orders and directions
2. The conception of a multistroke as inherently a unistroke, but where part of this unistroke is made away from the sensing surface
3. The recognition of 1D gestures (e.g., lines)
4. The use of bounded rotation invariance to support recognition of more symbols
5. An evaluation of $N on a set of handwritten algebra symbols made in situ by middle and high school students working with a math tutor prototype on Tablet PCs

In order to not be super annoying, and thus allow programmers to just draw a template with a single orientation and stroke order, $N automatically calculates and stores the "unistroke permutations" of the provided multistroke templates. This permutation treats the template as a unistroke design where part of the stroke occurs off of the drawing surface (think about it being invisible). They provide a nice example of this here:

Through their user study, it was found that $N had a 96.6% accuracy when using 15 templates per shape. Additionally, a 96.7% accuracy was obtained using 9 templates of the original gestures tested with $1.

Discussion:
If stroke order and direction are not important, then the $N recognizer seems to be pretty awesome. In some cases, as in accommodating left and right-handed users, the ability to match the final gesture is very important because it minimizes user frustration and increases system accuracy.