The many different mathematical techniques used to solve pattern recognition problems may be grouped into two general approaches: the decision-theoretic (or discriminant) approach and the syntactic (or structural) approach. 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. Applications of decision-theoretic approach include character recognition, medical diagnosis, remote sensing, reliability and socio-economics. A relatively new approach is the syntactic approach. In the syntactic approach, each pattern is expressed in terms of a composition of its components. The recognition of a pattern is usually made by analyzing the pattern structure according to a given set of rules. Earlier applications of the syntactic approach include chromosome classification, English character recognition and identification of bubble and spark chamber events. The purpose of this monograph is to provide a summary of the major recent applications of syntactic pattern recognition.
After a brief introduction of syntactic pattern recognition in Chapter 1, the nine main chapters (Chapters 2-10) can be divided into three parts. The first three chapters concern with the analysis of waveforms using syntactic methods. Specific application examples include peak detection and interpretation of electro-cardiograms and the recognition of speech patterns. The next five chapters deal with the syntactic recognition of two-dimensional pictorial patterns. Applications examples consist of Chinese character recognition, recognition of geometric figures and two-dimensional mathematical expressions, classification of finger-print patterns and interpretation of Earth Resources Satellite data. The last chapter treats the problem of recognition of three-dimensional objects, namely, machine parts and industrial objects.