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

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Summary

This text describes a symbol recognizer that can be easily trained based on single examples of the symbols. The recognizer bases on template matching as other recognizers discussed before on this post. The novel approach of this recognizer relies on two basic aspects: the combination of multiple template matcher recognizers to provide a more accurate final recognition result. And a rotation invariance achieved by the transformation of the unknown symbol into polar coordinates which greatly reduces the rotation invariance processing time compared with other techniques. The accuracy reported is remarkably high, and the recognizer has very good time response.
The recognizer begins with a pre-recognition based on the transformation of the unknown shape into polar coordinates where the stored templates that are too dissimilar to the presented shape can be pruned away. Thereafter the remaining templates can be compared using 4 different methods (Hausdorff Distance, Modified Hausdorff Distance, Tanimoto Similarity Coefficient, and Yule Coefficient) these classifiers return their more likely shapes and a final module standardizes each output and combines the outputs together to form a final decision.

Discussion

This paper presents interesting results based on existing techniques. I think that the mayor contribution is the prerecognition based on polar transformation of coordinates, this smart idea converts the problem of rotation to a more natural domain making it easy to handle. Many advantages come from template matching as explained in the paper such as overtracing support and dashed lines. However shapes that are not always drawn with the same geometric proportions may be a problem (e.g. arrows), nevertheless for a standard symbol domain this is usually not the case so it is a very good option.