Universal approximation using radial-basis-function networks
Neural Computation
Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Performance improvement of RBF network using ART2 algorithm and fuzzy logic system
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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In this paper, we proposed a novel hierarchical algorithm to recognize English calling cards. The algorithm processes multiresolution images of calling cards hierarchically to extract characters and recognize the characters by using an enhanced neural network method. Each processing step functions at lower overhead and results improved output. That is, first, horizontal smearing is applied to a 1/3 resolution image in order to extract the areas that only include characters from the calling card image. Second vertical smearing and the contour tracking masking, is applied to a 1/2 resolution image in order to extract individual characters from the character string areas. And last, the original image is used in the recognition step, because the image accurately includes the morphological information of the characters accurately. To recognize characters with diverse font types and sizes, the enhanced RBF network that improves the middle layer based on the ART1 was used. The results of experiments on a large number of calling card images showed that the proposed algorithm greatly improves the character extraction and recognition compared with traditional recognition algorithms.