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Character preclassification based on genetic programming
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DOI：10.1016/S0167-8655(02)00104-6.png)
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Abstract
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This paper presents a learning system that uses genetic programming as a toot for automatically inferring the set of classification rules to be used during a preclassification stage by a hierarchical handwritten character recognition system. Starting from a structural description of the character shape, the aim of the learning system is that of producing a set of classification rules able to capture the similarities among those shapes, independently of whether they represent characters belonging to the same class or to different ones. In particular, the paper illustrates the structure of the classification rules, the grammar used to generate them and the genetic operators devised to manipulate the set of rules, as well as the fitness function used to drive the inference process. The experimental results obtained by using a set of 10,000 digits extracted from the NIST database show that the proposed preclassification is efficient and accurate, because it provides at most 6 classes for more than 87% of the samples, and the error rate almost equals the intrinsic confusion found in the data set. (C) 2002 Published by Elsevier Science B.V.
Keywords:
character recognition
preclassification
genetic programming
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