Jonathan Hall
Summary
The paper aims to differetiate the significance of ink features for Diagram Recognition. 46 features from kinds of sources are tested in the paper to judge their importance when distinguishing shape and text. The core technique of differentiating is based on a decision tree. To realize a decision tree, the author uses rpart function in R Statistical Package. The best partitioning feature used in rpart is chosen by minimizing a measure of purity using the Gini index. The decision tree is the figure below:
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| The decision tree for differentiating features |
Through the experiment, 8 features are found to be the most important ones. They are Time till next stroke,Speed till next stroke, Distance from last stroke, Distance to next stroke, Bounding box width, Perimeter to area, Amount of ink inside, Total angle.
Discussion
The paper provides some important information when recognizing diagrams. Those 8 features mentioned in the paper seem useful in differentiating shapes and texts. It may be useful in Project2, helping me to distinguish shapes and texts.
However, I think the paper is limited in several ways. First, it is just a "beginner" paper, which just gives some hints in the diagram recognition. And the decision tree can only be used in binary classification. If there is another kind of elements in the diagram, how does the decision tree work? Second, the result seems not very good. There are still 42% misclassified shapes and 21% misclassified texts. The algorithm needs more improvement.
Yes, this seems very introductory. I didn't really understand the results, which were pretty inconclusive to me. I think this paper does do a good job of pointing out the shortcomings of sketch recognition, particularly with regards to the shape vs. text problem.
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