Title :
Pattern theory for character recognition
Author :
Jean, Jack S N ; Xue, Kefu ; Goel, Shailendra
Author_Institution :
Dept. of Comput. Sci. & Eng., Wright State Univ., Dayton, OH, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
Pattern theory is an engineering theory of algorithm design which provides a robust characterization of all types of patterns. Similar to logical neural networks, the theory can be used to generalize from a set of training data. However, it optimizes network architectures as well as the “weights” of the resulting machine. In this paper, the application of the theory to character recognition is considered. The application requires a simple extension to the theory and a faster algorithm to perform a basic decomposition operation. Such an algorithm is developed and described in the paper. Some simulation results of the algorithm are also included
Keywords :
character recognition; neural net architecture; optimisation; character recognition; decomposition; logical neural networks; network architecture optimization; pattern theory; robust characterization; Algorithm design and analysis; Application software; Character recognition; Data mining; Design engineering; Digital-to-frequency converters; Lifting equipment; Neural networks; Robustness; Training data;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374939