An image-based, trainable symbol recognizer for hand-drawn sketches

Paper
Kara, Levent Burak, and Thomas F. Stahovich. "An image-based, trainable symbol recognizer for hand-drawn sketches." Computers & Graphics 29.4 (2005): 501-517.
Publication Link: http://www.sciencedirect.com/science/article/pii/S0097849305000853

Summary

This paper takes a signal processing approach to sketch recognition, treating sketches in the same way as a digital image block and processing accordingly.  In this approach they address the typical problems with using a template matching approach (which was one of many approaches they could have used) such as scaling, rotation and translation. 

Discussion

Pros

They also give a more in depth description of the workings of their algorithms along with discussions on limitations of their work.
I thought their analysis was excellent and satisfactorily* thorough in a way that is missing in many of the other papers I've read. 

Cons.

The authors highlight that the performance of mean vs. median statistic, in this domain, results in a graceful vs. steep decay respectively, in their discussion of modified Hausdorff distance. Since this was a key contribution in their work, I believe they should show how that this is the case for their specific domain. I don't think the cited argument was sufficient.
Also, what is ink length? This is very vaguely described in a way that I do not understand.