Francisco
Summary
The paper introduces a graph-based (ARG graph) symbol recognizer.
Each symbols is represented by attributed relational graph, which describes its geometry and topology. A template or definition of a symbol is created by constructing an average ARG from lots of training samples.
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| A rectangle and its ARG |
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| Error Metrics and their corresponding weights |
Six error metrics are used to measure similarity. And each error metric is assigned a weight to represent its contribution when matching. Recognition is implemented by graph matching. Stochastic, error-driven, greedy and sort matching are all used to recognize symbols.
A user study is conducted to test the performance of four matching algorithms. The first three have similar performance of about 92% for top-one accuracy and about 97% for top three accuracy. Sorting, though has lower performance, is still a good choice for PDA or those devices whose computational resources are constrained.
Discussion
Graph-based symbol recognizer is very useful to deal with multi-stroke symbols, as shown in the paper. And it is also robust to rotation, translation, uniform or non-uniform scaling. But the problem is how to construct a correct graph for each symbol? In the paper, the author requires users to draw a symbol in the same order or should be similar to what he has drawn in training period, so there will be very little error when constructing the average ARG. I think it seems difficult for each user to do this. According to my experience on project #1 truss recognition, I also used a graph-based method to recognize a truss. I encountered a big problem, that users always draw a truss with some uncertain strokes. Their relationship cannot be judged easily. For graph-based method, it requires users to draw a symbol very carefully, that those connected ones must be connected.
Graph-based symbol recognizer is very useful to deal with multi-stroke symbols, as shown in the paper. And it is also robust to rotation, translation, uniform or non-uniform scaling. But the problem is how to construct a correct graph for each symbol? In the paper, the author requires users to draw a symbol in the same order or should be similar to what he has drawn in training period, so there will be very little error when constructing the average ARG. I think it seems difficult for each user to do this. According to my experience on project #1 truss recognition, I also used a graph-based method to recognize a truss. I encountered a big problem, that users always draw a truss with some uncertain strokes. Their relationship cannot be judged easily. For graph-based method, it requires users to draw a symbol very carefully, that those connected ones must be connected.
I guess the requirement to draw a sketch in the same order is a big problem in sketch recognition domain. I faced the same problem when doing the project 2 COA sketch recognition using fuzzy logic based recognition engine, where a single error in the way of drawings with different orders make the Paleo recognizer to fail the entire recognition process.
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