Reading #13 Ink Features for Diagram Recognition

Comment:
Chris
Summary:
This paper tried to solve very important problem in Sketch recognition : Distinguish text and shape. Acutally, most of document of sketched diagram consists of text and shape or drawing diagram. However, recognzing text and shape are two independant tasks that we need to reocognize seperately. So the first task is to find which part is text and which part is shape. In fact, this is very difficult problem and there is no universal solution for this problem. In this paper, the author shows very intuitive way to distinguish these two and gains reasonable accuracy but still not very high. The overall approach is very simple, is to find most disdinguishable features for differenciating shape and text. The author initially choose forty-six features, and use decision tree to find the most important features . The final feature set consists of eight most disdinguishing features. In the result part of this paper, the author compares his recognizer with Microsoft divider and Inkkit, and found that accuracy is much higher than these two.


Discussion:
For me, this paper is very informative from the perspective of choosing features. For different sketching system, we need to recognize different sybmols. For statistical pattern recognition, the most important task is to find most disdinguishing feature set. Using too few feature may result in low accuracy of sytem, choosing too many features instead results in overfitting. This paper gives me answer, approach used in this paper is a really nice way to find good features among all the candidate features. We can make automatic feature extraction system for certain domain using decision tree like in this paper.