| Summary: | 博士 === 國立交通大學 === 資訊工程研究所 === 83 === This dissertation is concerned with handwritten Chinese
character recognition problem. In the first part, we propose a
problem reduction technique to reduce the radical recognition
problem to a subproblem of recognizing stable stroke
substructure(s) in the radical. Furthermore, the stroke
substructure recognition problem can be further reduced to a
subproblem of identifying a salient stroke in each stroke
substructure. The actual recognition process will work in the
reversed order, i.e., from salient stroke toward radical. In
this problem reduction formulation, each subproblem deals with
a simpler but stabler stroke substructure than its original
problem, so we can find an easier and more reliable solution to
the subproblem. In the second part, we proposed a
preclassification technique for handprinted Chinese characters.
By using the stable stroke substructures contained in a
character as features, we can get a good classification result.
According to the radical recognition strategy mentioned above,
the stroke substructures can be expanded to a radical under the
guidance of knowledge base. There would be no overhead caused
by this proposed preclassification stage. Since there may be
some incorrect stroke features, including missing or incorrect
stroke intersection and connection relationships caused by
handwriting variance. These incorrect features will cause the
stroke substructures and radicals to be incorrectly recognized.
In the third part, we propose a graph-based approach to deal
with these variation problems. By matching subgraphs of a
character with graphs of predefined stroke substructures and
radicals, we can find all possible stroke substructures and
radicals in a character. Examples are included to illustrate
the ideas presented above. The performance of the algorithms is
also evaluated, and comparisons with some other existing
methods are made.
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