Character Recognition with Metasets
Bartomiej Starosta · InTech eBooks · 2011
The chapter presents a new approach to the character recognition problem. It is based on metasets – a new concept of sets with partial membership relation. By the character recognition problem we understand determining the similarity degree of the given character sample to the defined character pattern. The discussed mechanism may be applied not only to characters (e.g. letters), but to arbitrary data represented onmonochromatic images or even multi-dimensional figures. The theory of metasets brings a newmodel of “fuzzy”membership relation for sets. Ametaset may be a member of (or equal to) another metaset to variety of different degrees – contrary to classical sets where membership and equality are always either true or false. The goal of the chapter is to present the application of the new, abstract theory to solving a practical, well-known problem. It develops the method which was partially introduced for some particular case in (Starosta, 2009). The proposed solution had been implemented as a computer program. The experiments made with the program confirm that the theoretical assumptions are correct and the obtained results properly reflect our perception of similarity of characters. It should also be stressed that the concept of metaset itself was partially inspired by another computer application for character recognition, based on neural networks.