Comment:
Chris
Summary:
This is an another paper dealing with shape versus text. But it uses completely different method comparing to Reading#13. This paper only use entropy information to classify shape or text. In reading#13, the author use decision tree to find the most disdinguishable features and use these features to classify shape or text. However, in this paper, author use entropy measure, which represents uncertainty measurement, to classify shape or text. Paper shows that text is more randomly structured, which means has larger uncertainty rate than shape. Using this intution they create a code mapping model. They use angle between two consecutive points to represents the whole stroke, and these angles are put into seven different angle beams. Thus, after this processing, each stroke can be represented as sequence of characters -string. The next is to compute entropy value for this string, and they use this value to classify stroke as shape or text. For grouping strokes step, they simply use temporal information to group strokes. The result system shows good accuracy rate for shape vs text .
Discussion :
Smart intution & idea!! Idea is great, and very benificial for other applications. However.... I am wondering if it gets high accuracy in most of cases. This algorithms greatly depends on how the shape drawn, if the shape itself is very complex, and drawn cursively, the algorithms cannot classify it. And the grouping approach is not good.
The most ideal way to accomplish this task is , in my option, utilizing context information. Without the aid of context information, any recognition system cannot gain acceptable recognition rate.
Anyway, I have to say the idea is good, it is definitely a nice paper.
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