Self-generation ART neural network for character recognition
- Authors
- Kim, Taekyung; Lee, Seongwon; Paik, Joonki
- Issue Date
- 2006
- Publisher
- SPRINGER-VERLAG BERLIN
- Citation
- ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 2, PROCEEDINGS, v.3972, pp 277 - 286
- Pages
- 10
- Journal Title
- ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 2, PROCEEDINGS
- Volume
- 3972
- Start Page
- 277
- End Page
- 286
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/40220
- DOI
- 10.1007/11760023_41
- ISSN
- 0302-9743
- Abstract
- In this paper, we present a novel self-generation, supervised character recognition algorithm based on adaptive resonance theory (ART) artificial neural network (ANN) and delta-bar-delta method. By combining two methods, the proposed algorithm can reduce noise problem in the ART ANN and the local minima problem in the delta-bar-delta method. The proposed method can extend itself based on new information contained in input patterns that require nodes of hidden layers in neural networks and effectively find characters. We experiment with various real-world documents such as a student ID and an identifier on a container. The experimental results show that the proposed self-generation. ART algorithm reduces the possibility of local minima and accelerates learning speed compared with existing.
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Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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