Wednesday, December 15, 2010

Reading #15. An image-based, trainable symbol recognizer for hand-drawn sketches (Kara)

COMMENTS:

Paco

SUMMARY:

This paper discusses an image-based symbol recognizer that represents symbols as bitmap images.  The input symbols are cropped and turned into templates.  The template is then turned into polar coordinate representation for optimal orientation and compared to the definitions.  If there is a match then recognition switches to screen coordinates and definitions are analyzed using four classifiers based on template matching techniques.
The approach presented here does not rely on segmentation of strokes and therefore does not have difficulty recognizing symbols drawn with missing strokes, multiple strokes, and/or overtracing.  The accuracy for this recognizer was 95.7% in a user-dependent setting on a set of 20 symbols; the accuracy rate was 94.7% in a user-dependent setting.


DISCUSSION:


This paper presents a really interesting approach to the orientation challenge usually encountered when trying to do template matching.  Instead of rotating the template in small increments and doing a comparison to check for a match each time, the authors use the templates' polar coordinates to determine the translation between the patterns.  As the authors mentioned, I've seen this method used on rigid, well-defined objects, but not for sketched symbols.  This may have been my favorite part of the paper because it was explained very well...good reading choice.

No comments:

Post a Comment