Abstract :
Presents an approach to automatic construction of structural models incorporating discontinuous transformations, with emphasis on application to unconstrained handwritten character recognition. The author considers this problem as constructing inductively, from the data set, some shape descriptions that tolerate certain types of shape transformations. The approach is based on the exploration of complete, systematic, high-level models on the effects of the transformations, and the generalization process is controlled and supported by the high-level transformation models. An analysis of the a priori effects of commonly occurring discontinuous transformations is carried out completely and systematically, leading to a small, tractable number of distinct cases. Based on this analysis, an algorithm for the inference of super-classes under these transformations is designed. Furthermore, through examples and experiments, the author shows that the proposed algorithm can generalize unconstrained handwritten characters into a small number of classes, and that one class can represent various deformed patterns
Keywords :
character recognition; handwriting recognition; learning (artificial intelligence); automatic construction; deformed patterns; discontinuous transformations; shape descriptions; shape transformations; structural models; unconstrained handwritten character recognition; Algorithm design and analysis; Artificial intelligence; Cause effect analysis; Character recognition; Handwriting recognition; Inference algorithms; Pattern analysis; Pattern recognition; Prototypes; Shape;
