Recent Advances in Syntactic Pattern Recognition

The many different mathematical techniques used to solve pattern recognition problems may be grouped into two general approaches [1,2]. They are the decision-theoretic (or discriminant) approach and the syntactic (or structural) approach [3]. In the decision-theoretic approach, a set of characteristic measurements, called features, are extracted from the patterns. Each pattern is represented by a feature vector, and the recognition of each pattern is usually made by partitioning the feature space. On the other hand, in the syntactic approach, each pattern is expressed as a composition of its components, called sub-patterns and pattern primitives. This approach draws an analogy between the structure of patterns and the syntax of a language. The recognition of each pattern is usually made by parsing the pattern structure according to a given set of syntax rules. In this paper, we briefly review the recent progress in syntactic pattern recognition and some of its applications.

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