The new paradigm of syntactic pattern recognition, SPR, which uses multi-derivational parsing of vague languages is introduced in the paper. The methodology proposed addresses the issue of the recognition of vague/distorted patterns which is one of the important open problems in the area. The concept of the vague language of patterns and the efficient parsing method based on the class of dynamically programmed grammars are introduced. A vague language is defined with vague primitives which are vectors of "neighboring" primitives associated with measures of distance, probability, fuzziness, etc. The use of vague primitives allows us to identify b best structural templates during multi-derivational parsing that can be used for getting more adequate final result. The generic architecture of SPR system based on the approach proposed together with the system's applications for short-term electrical load forecasting and for analysis of ultrasound images in order to diagnose congenital defects of fetal palates are presented. The results of the experimental studies are discussed.

Abstract The method of the learning of vague languages which represent distorted/ambiguous patterns is proposed in the paper. The goal of the method is to infer the quasi-context-sensitive string grammar which is used in our model as the generator of patterns. The method is an important component of the multi-derivational model of the parsing of vague languages used for syntactic pattern recognition.

Fundamental open problems, which are frontiers of syntactic pattern recognition are discussed in the paper. Methodological considerations on crucial issues in areas of string and graph grammar-based syntactic methods are made. As a result, recommendations concerning an enhancement of context-free grammars as well as constructing parsable and inducible classes of graph grammars are formulated.