Reading #18: Spatial Recognition and Grouping of Text and Graphics (2004)

by Michael Shilman and Paul Viola

Comments: Marty

This paper discusses grouping and recognition of sketch diagrams. They take a big canvas and identify many different symbols in it. This is cool. You can draw the stuff in any order and it will segment out each symbol. This is hard and probably the main contribution of the paper, but I am sleepy. The grouping had 99% accuracy.. sweet man. Also, the recognition and the grouping were 97% accuracy.

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I am dealing with the segmentation problem in hand gestures, and I can relate to this problem. It is nice to have this problem solved with a high accuracy. This makes more complicated sketch interaction possible.

Reading #17: Distinguishing Text from Graphics in On-line Handwritten Ink (2004)

by Christopher M Bishop, Groffrey E Hinton

Comments: Marty

This is an earlier text vs shape paper. It uses both stroke features, gaps, and time data to help separate text from other strokes. They used HMMs for recognition. They collected data from some dudes. The dudes drew some stuff, whatever they wanted, as long as the sketches contained some text elements and some non-text strokes. Recognition results were mixed, with some groups getting in the mid 90s and some in the mid 70s.

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Shape v Text is a hard problem, and there are many solutions to solve it. I don't like this gaps and time solution, however. It just doesn't make sense to me... I think I would like entropy better or simply visual approaches. Also, I think we can combine gestures into the mix to denote text. blah blah blah

Reading #16: An Efficient Graph-Based Symbol Recognizer (2006)

by WeeSan Lee, Levent Burak Kara, Thomas F Stahovich

Comments: Ozgur

This paper takes a graph-based approach to sketch (symbol) recognition and explored several graph matching techniques. They compute many error metrics for matching graphs and represent symbols using graphs. They collected several types of symbols from some users and ran their 4 matching algorithms on the data, getting results in the mid- to high-90s for most algorithms.

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Some symbols can naturally be represented as graphs. We have seen that graph matching can yield high accuracy for appropriate shapes. I think that this could be one component of a good general purpose recognizer.