Reading #16: An Efficient Graph-Based Symbol Recognizer

COMMENTS ON OTHERS:

            JJ

SUMMARY

              The paper proposes a symbol recognizer for pen based user interfaces which the symbols are attributed to relational graphs and best matches of the unknown symbol based on that. The graph matching is done using four techniques, stochastic matching, error driven matching, greedy matching and sort matching.  In an attributed relational graph (ARG) each node represents the geometric primitives, and the edges represent the geometric relationship between them. A definition for a symbol is created by constructing an average ARG from a set of training samples. The proposed recognition applies the ARG of unknown symbol to compare it with ARG of each definition symbol to find a best match based on above defined graph matching techniques. 

DISCUSSION

             The authors claim that stochastic, error-driving matching, and greedy are search based classifiers and Sort is based on orientation fixed method. This means that the other 3 methods are orientation invariant? But the figure 3 actually presents a problem with searching where the orientation brings a dissimilar pair into the matching at the comparison.