COMMENTS ON OTHERS:
Jianjie (JJ) Zhang.
SUMMARY
The paper proposes a method to distinguish between shape and text strokes based on entropy rate. The authors state that the entropy rate is significantly higher for text strokes compared to shape strokes. They propose a single feature-zero-order entropy rate with a correct classification rate of 92.06% with a trained threshold. The zero-order entropy is determined independently from the previous symbol of the sketch point. The symbol refers to an associated symbol value for each point’s angle between two points of a text or a shape.
DISCUSSION
The authors argued that using an arbitrary set of features and trial and error approach is time consuming, which I totally agreed. I had the same in mind when reading the Ink feature based recognition on statistical analysis. Time is something to worry in a sketch system with both text and shapes, not only to distinguish them but also to recognize them from the library sketches. The proposed system deems to be a simpler way to achieve the text and shape classification, but its still not the best solution when it comes to complex shapes, which quite similar in entropy even to a simple character set.
1 comment:
It seems like the accuracy/time tradeoff is more prominent in the shape vs. text recognition area than some of the other subsets of sketch recognition that we have read about.
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