An efficient algorithm for the inexact matching of ARG graphs using a contextual transformational model

The paper illustrates an algorithm for the inexact matching of attributed relational graphs. A sample graph is considered matchable with one of the prototypes if, by using a defined set of syntactic and semantic transformations, it can be made isomorphic to the graph of the prototype. The applicability of a transformation is contextually defined, i.e. each transformation can be defined with reference to a prototype, and can be applied only when the sample graph is being matched with that prototype. The reduction of the computational complexity with respect to a brute-force approach is given with reference to an OCR application.