Abstract
A new approach to character recognition problem, based on meta sets, is introduced and developed. For the given compound character pattern consisting of a number of character samples accompanied by their corresponding quality degrees, and for the given testing character sample, the main theorem of the paper gives means to evaluate the correlation between the testing sample and the compound pattern. It also enables calculation of similarity degrees of the testing sample to each pattern element. The quality degrees and the correlation are expressed by means of membership degrees of meta sets representing samples in the meta set representing the compound pattern. The similarity degrees are expressed as equality degrees of these meta sets.
The meta set theory is a new alternative to the fuzzy set theory. By the construction of its fundamental notions it is directed to efficient computer implementations. This paper presents an example of application of the theory to a real-life problem.
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Starosta, B. (2009). Application of Meta Sets to Character Recognition. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds) Foundations of Intelligent Systems. ISMIS 2009. Lecture Notes in Computer Science(), vol 5722. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04125-9_63
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DOI: https://doi.org/10.1007/978-3-642-04125-9_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04124-2
Online ISBN: 978-3-642-04125-9
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