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

COMMENTS ON OTHERS:

           JJ  

SUMMARY

              The paper proposes a trainable, hand-drawn symbol recognizer based on multi layer recognition scheme with the symbols internal representation on a binary template. Ensembles of four different classifiers are used o rank symbols based on similarity to an unknown symbol and the scores are aggregated to produce a combined score. The best score is assigned to the unknown symbol. All four classifiers are template matching techniques to compute the similarity between symbols. The authors used a polar coordinate based technique to compensate the rotation sensitiveness of the template matching technique. The authors state that the proposed system is particularly useful for sketchy inputs like heavy over stroking and erasing due to its binary template approach. 

DISCUSSION

             The authors state that the binary template approach is useful in sketchy overly stroked and erased sketches, but the down sampling and framing it to a 48x48 is questionable whether the best approach. May be authors should experiment more with other techniques like binarizing the ink features and then getting a skeleton of the bits which will actually preserve the sketch input than reducing it.