Application of Meta Sets to Character Recognition

Autor:	Bartłomiej Starosta
Rok vydání:	2009
Předmět:	business.industry Computer science Fuzzy set Sample (statistics) Pattern recognition Set (abstract data type) Correlation Character (mathematics) Similarity (network science) Artificial intelligence Set theory Element (category theory) business Algorithm
Zdroj:	Lecture Notes in Computer Science ISBN: 9783642041242 ISMIS
DOI:	10.1007/978-3-642-04125-9_63
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::82c6439bca8f5e99774cbc920298a16f https://doi.org/10.1007/978-3-642-04125-9_63 Zobrazit plný text záznamu