A structural indexing method for character recognition

In the framework of structural character recognition, the authors present a method to reduce the number of prototypes to match with a given sample. The basic idea is that a coarse description of the sample, even if not adequate for the recognition, can be powerful enough to discriminate among the prototypes those that most likely will match the sample. Once this subset has been found, a more detailed description is computed, and the main classification step entered. To achieve the purpose, a multilevel description of the character, in terms of the features provided by the feature extractor. At the intermediate level, the character is decomposed into components by removing the branch points. Eventually, each component is further split into simple, meaningful parts called superfeatures. By using the highest level of the description a fast and reliable selection of the prototypes to be considered as candidates for the matching can be obtained, while the lowest one is used by the main classifier to choose which one of the prototypes, among the selected ones, has the best matching with the sample. Experiments have proved that the method is correct and efficient. It is correct since it makes it possible to select a subset of prototypes which always contains the right one, and it is efficient since it significantly reduces the number of prototypes to be matched with the sample