Structural Indexing for Character Recognition

摘要

In this paper we present a structural method to speed up the character recognition process by reducing the number of the prototypes used during the classification of a given sample. It adopts simplified descriptions of the character shapes and uses a rough classification scheme in order to select the prototypes that most likely will match a given sample. The descriptions are stored in a multilevel data structure adopted to represent the character shape. The lowest level of such a data structure contains the detailed description of the character in terms of its skeletal features. The intermediate one consists of a list of groups of features, each one representing a character component. The upper level, eventually, is an index vector, whose dimension equals the different types of superfeatures. By using this index vector a fast and reliable selection of the prototypes to be considered as candidates for the matching can be obtained. Once this subset has been obtained, the more detailed description based on the skeletal features is resorted and the main classifier activated. Experiments have proved that the method is efficient and correct, since it allows us to select a small subset of prototypes which always contains the right one, thus reducing the classification time without affecting the accuracy of the system.