Reading 8#: A Lightweight Multistroke Recognizer for User Interface Prototypes

Comments:
Jonathan Hall
Summary:
$N, a good extension of $1, aims to recognize multistroke sketches. The key idea of $N is to treat multistroke as kinds of unistroke by connecting their end points together. Besides, $N can recognize the mixture of multistroke, unistroke and 1D gestures. Also, $N is able to recognize orientation-dependent and orientation-independent sketches.

$N treated a n-component multistroke  sketch as 2^n*n! kinds of unistroke sketches, according to the stroke order and stroke direction. A large number of sketches were produced. However, $N adopted optimization using the start angle and the number of strokes. The start angle reduced unistroke comparison by 79%  and increased accuracy by 1.3%. The number of strokes reduced an additional 10.4% comparison, and increased an additional accuracy by 1.7%.

$N solved 1D sketch recognition by ratio of sides of the bounding box. If the ratio was less than a threshold, then the sketch was  scaled to preserve aspect.  In order to recognize orientation-dependent and orientation-independent sketches, the system required users to flag sketches when training. For those orientation-dependent sketches, they would be rotated from their original angles, otherwise from 0.

$N still had some limitation, like lacking provisions for scale or position dependence and not recognizing sketches whose gestalt is their appeal.

$N was tested by youth from middle and high school classrooms, and got 96.6% accuracy on the algebra symbols.

Discussion:
$N is a great work,and great extension of $1. $N can recognize multistroke with only 200 lines of codes,high accuracy and high speed. It should be a milestone in multistroke-sketch recognition. Besides, tricks in dealing with orientation and 1D gestures are also very impressive.

A pity is that $N is also a writer-dependent fashion as "future work" said. An important feature of a recognizer is to have some kind of generalization and to recognize other users' sketches. $N is still be further studied in future.

I am still skeptical with "indicative angle". The indicative angle is totally determined by the centroid and the start point. If the start point has some noise, or users begin with a wrong start angle, the recognition should be affected. Maybe the indicative angle should be more robust.