Random Graph Languages for Distorted and Ambiguous Patterns: Single Layer Model

Abstract

The work introduces a linguistic based model designed for distorted or ambiguous patterns where a graph based approach is used for structure representation. The knowledge about unevenness is usually created on the basis of finite number of patterns treated as positive samples of unknown language. The IE graphs are used as the base. Single pattern can be represented using deterministic IE graph. Subsequently, the collection of patterns, represented by deterministic graph is transformed into equivalent random graph language. Utilization of the grammatical inference mechanisms gives the possibility to perform this process in automatic way. Using the IE graphs and imposing some simple limitations on graph structures allows to obtain a polynomial complexity of knowledge inference. In the work it is described how to use the proposed model for collecting the knowledge in handwritten signatures recognition and analysis systems. Information about graphemes (solid fragment of handwritten signature) variability is stored in the form of random IE graphs and stochastic ETPL(k) graph grammars. Instead of an ordinary the IE graph, an attributed one is used in order to increase a descriptive power of the proposed schema. The parametrical data embedded in the graph carries some additional semantic information associated with the structure of pattern. The work presents discussion about inference scheme and computational complexity of the proposed linguistic representation scheme. Described methodology can be especially suited for creating the knowledge representation of the handwritten signatures, signs and ideograms (e.g. kanji) in offline recognition systems.