@inproceedings{bd59821b51fb4d6b86f81c1ae4cb0ef6,
title = "A class-modularity for character recognition",
abstract = "A class-modular classifier can be characterized by two prominent features: low classifier complexity and independence of classes. While conventional character recognition systems adopting the class modularity are faithful to the first feature, they do not investigate the second one. Since a class can be handled independently of the other classes, the class-specific feature set and classifier architecture can be optimally designed for a specific class. Here we propose a general framework for the class modularity that exploits fully both features and present four types of class-modular architecture. The neural network classifier is used for testing the framework. A simultaneous selection of the feature set and network architecture is performed by the genetic algorithm. The effectiveness of the class-specific features and classifier architectures is confirmed by experimental results on the recognition of handwritten numerals.",
author = "Oh, \{Il Seok\} and Lee, \{Jin Seon\} and Suen, \{Ching Y.\}",
note = "Publisher Copyright: {\textcopyright} 2001 IEEE.; 6th International Conference on Document Analysis and Recognition, ICDAR 2001 ; Conference date: 10-09-2001 Through 13-09-2001",
year = "2001",
doi = "10.1109/ICDAR.2001.953756",
language = "English",
series = "Proceedings of the International Conference on Document Analysis and Recognition, ICDAR",
publisher = "IEEE Computer Society",
pages = "64--68",
booktitle = "Proceedings - 6th International Conference on Document Analysis and Recognition, ICDAR 2001",
}