Reading #15: An Image-Based, Trainable Symbol Recognizer for Hand-drawn Sketches (2005)

by Levent Burak Kara, Thomas F Stahovich

Comments: Jonathan

This paper takes an image-based approach to sketch recognition using an ensemble classifier consisting of four different classifiers. They want a system that can recognize sketches very fast (real time for interaction) and that is also rotation invariant (using a fast polar coordinate technique).

This paper really focuses on the sketch interface and making it an attractive alternative to paper. To be a viable alternative, interaction (and therefore recognition) must be able to occur in real-time with no interruptions to the user. They also want to be able to recognize many shapes as well as "sketchy" shapes.

They used 20 shapes collected from some users. They achieved recognition rates in the mid to high 90s.

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This is a good paper for an introduction to image-based approaches. It is also useful for understanding sketch interfaces. Considering the year (2005), the sketches were recognized very quickly and would be recognized even faster on today's machines.

Reading #14: Using Entropy to Distinguish Shape Versus Text in Hand-Drawn Diagrams (2009)

by Akshay Bhat and Tracy Hammond

Comments: Ayden

The authors propose that entropy rates are higher for text strokes than for non-text strokes and attempt to separate shapes from text using this idea. They achieved a 92% recognition rate. They define entropy, calculate entropy for all letters of the alphabet, and perform classification on collected sketches.

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I agree that text shapes have high entropy, and it is interesting to note that this approach has not been taken earlier in the history of sketch recognition. Obviously some primitive shapes, such as circle and rectangle, will have lower entropy than text, but what about helixes or more complex shapes? This might be good in some diagramming domains.

Reading #13: Ink Features for Diagram Recognition

by Rachel Patel, Beryl Plimmer, John Grundy, and Ross Ihaka

Comments: Jianjie

This paper aims to perform more accurate diagram recognition by performing a statistical analysis of features used for recognizing various diagram components from sketched samples. This is pretty much an introduction to some of the important concepts in sketch recognition and illustrates some general approaches to sketch recognition. The paper particularly focuses on shape vs. text.

The authors took 46 features grouped into 7 categories. They collected some sketches from 26 participants which contained some diagram elements and text. They used a statistical partitioning technique to find which features can best split the strokes into shape or text strokes and then constructed decision trees with significant features toward the start of the tree.

They tested their methods with some existing shape v text systems and found some interesting results...

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Sketch recognition still remains in its infancy despite its age, and formal analyses like this are important to help us understand the processes and achieve greater recognition performance. This work seems kind of inconclusive, however, and I didn't understand the results very well.