Abstract
In this paper, an optical character recognition system for hand-written rotated digits in land registry maps is presented. It is based on a neural network and trained by a constructive learning rule, the Hyperbox Perception Cascade (HPC). The HPC classifier can design complex, possibly nonconvex, disjoint, and bounded decision regions and treat the rejection problems of outliers and unanticipated patterns, which would otherwise tend to be classified positively in an incorrect class. We use “shape features” and a novel approach to select the most promising features to attain a low generalization error. The numerous experiments show that a subset of 24 of the 46 features obtains a good classifier with a high rate of correct classification and a low rate of rejection.
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Calabró, C., Ferragina, P. & Granieri, M.N. Recognition of hand-written rotated digits by neural networks. Machine Vis. Apps. 8, 351–357 (1995). https://doi.org/10.1007/BF01211497
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DOI: https://doi.org/10.1007/BF01211497