Citation
L. Kara, T. Stahovich. An Image-Based, Trainable Symbol Recognizer for Hand-Drawn Sketches. Computers & Graphics 29.4 (2005): 501-517.
Summary:
A trainable hand drawn symbol recognizer is described. The approach is useful for sketchy inputs as it is based on binary templates. This paper is designed around four different similarity methods to enhance recognition accuracy. These are Hausdorff distance, Modified Hausdorff distance, Tanimoto coefficient and Yule coefficient. During matching, distance maps are used as look-up tables for the closest distances. Polar coordinate transformation technique is used for rotation invariant recognition. Next step is examining remaining definitions in screen coordinates.
Discussion:
The idea of use of polar coordinate transformation for rotation invariant recognition is very impressive. The accuracy achieved, which is 95% is really commendable. The sensitivity to non-uniform scaling portrays the importance of topology over shape.
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