Using Entropy to Distinguish Shape Versus Text in Hand-Drawn Diagrams

Paper
Bhat, Akshay, and Tracy Hammond. "Using Entropy to Distinguish Shape Versus Text in Hand-Drawn Diagrams." IJCAI. Vol. 9. 2009.
Direct Link: http://ijcai.org/papers09/Papers/IJCAI09-234.pdf

Summary

In this paper, authors use entropy as a singular measure to distinguish between text and shapes in sketches.
The end product is a fairly accurate system that also outputs confidence for classification.

Discussion

Pros

A very elegant solution. I like the approach of encoding angular changes between smoothed stroke points.

Cons.

I can't think of any. Perhaps elaborating on some of the empirical decisions such as value for 'k' and other parameters and the effects of using alternative parameters would have been interesting.

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.

K-sketch: a'kinetic'sketch pad for novice animators

Paper
Davis, Richard C., Brien Colwell, and James A. Landay. "K-sketch: a'kinetic'sketch pad for novice animators." Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2008.
Publication Link: http://dl.acm.org/citation.cfm?id=1357122

Summary

This paper discusses the development and assessment of an animation tool for novices and experts. The paper covers requirements gathering, discusses major themes that intersect the needs of expert animators, and novices (people who want to but haven't delved into animation). The work is compared with Flash and more thoroughly with Microsoft Powerpoint.

Discussion

Pros

A very thorough user requirements phase and a sizable user evaluation. The authors give a clear description of the final features that were incorporated into the tool and how and why they were selected.

Cons.

I am not sure that either comparison (Flash or Microsoft Powerpoint) was appropriate. Flash, as the authors indicate, is very complicated for a novice, while Microsoft is not primarily an animation tool. And so one would expect it to perform poorly when compared with a tool that's primarily designed for animation.

iCanDraw: using sketch recognition and corrective feedback to assist a user in drawing human faces

Paper
Dixon, Daniel, Manoj Prasad, and Tracy Hammond. "iCanDraw: using sketch recognition and corrective feedback to assist a user in drawing human faces." Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2010.
Publication Link: http://dl.acm.org/citation.cfm?id=1753459

Summary

This paper applies sketch recognition methods to build an automated tool for learning how to draw. The paper describes two iterations of the work and discusses what works and what didn't, along with a brief description of template matching algorithms used in the interface.

Discussion

Pros

I thought it was an interesting and intuitive domain for applying sketch recognition.

Cons.

I thought the evaluations were simplistic and had a small sample size. They also do not compare their work with any existing work to form some type of baseline for their claims.

Sketch recognition algorithms for comparing complex and unpredictable shapes

Field, Martin, et al. "Sketch recognition algorithms for comparing complex and unpredictable shapes." IJCAI. 2011.
Direct Link: http://www.researchgate.net/profile/Tracy_Hammond/publication/220812144_Sketch_Recognition_Algorithms_for_Comparing_Complex_and_Unpredictable_Shapes/links/0deec529f76e510edf000000.pdf

Summary

This work describes the algorithms used in a previously discussed paper, which itself discusses a sketch-based tutoring system, Mechanix. In this work, the authors describe the algorithms used and provide some detail on the accuracy and results for a two class (match vs. no match) classification problem.

Discussion

Pros

Paper is well written and organized. Authors provide sufficient motivation for their work, as well as a good review of previous related work.

Cons.

I found it difficult to understand the use of BFS as described in the paper (even though I understand in principle how and why their approach works, this is not well communicated in the paper).

While the two class problem breakdown works well in the intended application, perhaps it would have been interesting to understand where the system fails (FP and FNs), the 'why' and the 'future' work suggestions.

Mechanix: a sketch-based tutoring and grading system for free-body diagrams

Paper
Valentine, Stephanie, et al. "Mechanix: a sketch-based tutoring and grading system for free-body diagrams." AI Magazine 34.1 (2012): 55.
Publication Link: http://www.aaai.org/ojs/index.php/aimagazine/article/view/2437

Summary

This paper describes the implementation and deployment test results for a domain specific, sketch-based tutoring system, Mechanix. The system, unlike other systems, offers machine intelligence as well as free hand sketch input as solutions to instructor provided truss problems, which are a major feature in some large introductory engineering courses where student enrollment is high, and individual feedback is important but limited.

Discussion

Pros

The paper is very well organized and provides a very thorough motivation for the work and a fairly detailed literature review. The authors conducted some experiments testing the overall function of the system to get a sense of impact on student performance, and enrollment. They also provide performance metrics on recognition accuracy of the system.

Cons

I feel that, since the major advantage of said system is in grading and providing feedback, the authors should have spent a little more time in the paper discussing the system performance on these specific functions vs. the overall system performance. What are the problem areas / challenges in recognition that they encountered during deployment? Providing some idea on how the feedback mechanism was used, and metrics collected to evaluate its performance would be useful to read and as fodder for future work in system improvement.