@inproceedings{2cfaaf79cc1646aaaa4f75cdbae7a4b9,
title = "Active handwritten character recognition using genetic programming",
abstract = "This paper is intended to demonstrate the effective use of genetic programming in handwritten character recognition. When the resources utilized by the classifier increase incrementally and depend on the complexity of classification task, we term such a classifier as active. The design and implementation of active classifiers based on genetic programming principles becomes very simple and effcient. Genetic Programming has helped optimize handwritten character recognition problem in terms of feature set selection. We propose an implementation with dynamism in pre-processing and classification of handwritten digit images. This paradigm will supplement existing methods by providing better performance in terms of accuracy and processing time per image for classification. Different levels of informative detail can be present in image data and our proposed paradigm helps highlight these information rich zones. We compare our performance with passive and active handwritten digit classification schemes that are based on other pattern recognition techniques.",
author = "Ankur Teredesai and J. Park and Venugopal Govindaraju",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; 4th European Conference on Genetic Programming, EuroGP 2001 ; Conference date: 18-04-2001 Through 20-04-2001",
year = "2001",
doi = "10.1007/3-540-45355-5\_30",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "371--379",
editor = "Julian Miller and Marco Tomassini and Lanzi, \{Pier Luca\} and Conor Ryan and Tettamanzi, \{Andrea G.B.\} and Langdon, \{William B.\}",
booktitle = "Genetic Programming - 4th European Conference, EuroGP 2001, Proceedings",
address = "Germany",
}