Turkish Journal of Electrical Engineering and Computer Sciences
Character Recognition Using Subpattern Coding in Neural Networks
Artificial neural networks can achieve high computation rates by using a large number of processing units with a high degree of connectivity between them. Network parameters are computed in such a way that they cause the network to converge to an equilibrium representing a solution. The aim of this paper is to give a novel biologically motivated, computationally efficient approach to character recognition using subpattern coding. Each character is decomposed into a number of different sizes of regions corresponding to subpatterns. While similarity between the character patterns considered as a whole is usually weak, it is commonly possible to obtain a great deal of similarity between their subpatterns. If similarity of subpatterns of various characters is greater than a certain level, they are assigned the same portion of character code and stored only once in a neuron. The stored subpattern together with the respective code portion is called a leaf. This similarity reduces the storage requirement for the leaf data and the testing time. During testing, the code representing a character pattern is retrieved instead of the character subpatterns which are distributed all over the leaves kept in an associative memory. Keywords: Associative memory, subpattern, leaf, similarity, correlation, stimulus.
Associative memory, subpattern, leaf, similarity, correlation, stimulus.
YAZICI, Rıfat and KARAL, Hasan (1996) "Character Recognition Using Subpattern Coding in Neural Networks," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 4: No. 1, Article 2. Available at: https://journals.tubitak.gov.tr/elektrik/vol4/iss1/2