Thursday, December 16, 2010

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

Summary

A multi stroke hand drawn symbol recognizer is proposed here. Their technique shows a way to learn symbols definitions using prototype examples allowing users to train new symbols.

Their method has two steps. First, polar coordinates are used to determine angular alignment and eliminate unlikely definitions. Next, the surviving definitions are tested using the normal screen coordinates with four template classifiers. The results of individual classifiers are combined to produce the recognizer’s final decision.

They test their system using two user study. First, on numeric digits where they performed only slightly worse than dedicated recognizers. The second test was in the engineering symbols domain such as resistors, transistors, integral symbol, etc. Their accuracy for being in the top 2 of the result was above 96% in all the tests they conducted in this domain.

Discussion

This paper used some interesting distance measures and ideas which I think I could use earlier in my projects if I had known them.

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