IEICE TRANSACTIONS on Fundamentals
Recognition of English Calling Cards by Using Enhanced Fuzzy Radial Basis Function Neural Networks
Summary :
In this paper, we proposed the novel method for the recognition of English calling cards by using the contour tracking algorithm and the enhanced fuzzy RBF (Radial Basis Function) neural networks. The recognition of calling cards consists of the extraction phase of character areas and the recognition phase of extracted characters. In the extraction phase, first of all, noises are removed from the images of calling cards, and the feature areas including character strings are separated from the calling card images by using the horizontal smearing method and the 8-directional contour tracking method. And using the image projection method the feature areas are split into the areas of individual characters. We also proposed the enhanced fuzzy RBF neural network that organizes the middle layer effectively by using the enhanced fuzzy ART neural network adjusting the vigilance parameter dynamically according to the similarity between patterns. In the recognition phase, the proposed fuzzy neural network was applied to recognize individual characters. Our experiment result showed that the proposed recognition algorithm has higher success rate of recognition and faster learning time than the conventional RBF network based recognitions.
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- IEICE TRANSACTIONS on Fundamentals Vol.E87-A No.6 pp.1355-1362
- Publication Date
- 2004/06/01
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- Online ISSN
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- Special Section PAPER (Special Section on Papers Selected from 2003 International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC 2003))
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Kwang-Baek KIM, Young-Ju KIM, "Recognition of English Calling Cards by Using Enhanced Fuzzy Radial Basis Function Neural Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E87-A, no. 6, pp. 1355-1362, June 2004, doi: .
Abstract: In this paper, we proposed the novel method for the recognition of English calling cards by using the contour tracking algorithm and the enhanced fuzzy RBF (Radial Basis Function) neural networks. The recognition of calling cards consists of the extraction phase of character areas and the recognition phase of extracted characters. In the extraction phase, first of all, noises are removed from the images of calling cards, and the feature areas including character strings are separated from the calling card images by using the horizontal smearing method and the 8-directional contour tracking method. And using the image projection method the feature areas are split into the areas of individual characters. We also proposed the enhanced fuzzy RBF neural network that organizes the middle layer effectively by using the enhanced fuzzy ART neural network adjusting the vigilance parameter dynamically according to the similarity between patterns. In the recognition phase, the proposed fuzzy neural network was applied to recognize individual characters. Our experiment result showed that the proposed recognition algorithm has higher success rate of recognition and faster learning time than the conventional RBF network based recognitions.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e87-a_6_1355/_p
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@ARTICLE{e87-a_6_1355,
author={Kwang-Baek KIM, Young-Ju KIM, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Recognition of English Calling Cards by Using Enhanced Fuzzy Radial Basis Function Neural Networks},
year={2004},
volume={E87-A},
number={6},
pages={1355-1362},
abstract={In this paper, we proposed the novel method for the recognition of English calling cards by using the contour tracking algorithm and the enhanced fuzzy RBF (Radial Basis Function) neural networks. The recognition of calling cards consists of the extraction phase of character areas and the recognition phase of extracted characters. In the extraction phase, first of all, noises are removed from the images of calling cards, and the feature areas including character strings are separated from the calling card images by using the horizontal smearing method and the 8-directional contour tracking method. And using the image projection method the feature areas are split into the areas of individual characters. We also proposed the enhanced fuzzy RBF neural network that organizes the middle layer effectively by using the enhanced fuzzy ART neural network adjusting the vigilance parameter dynamically according to the similarity between patterns. In the recognition phase, the proposed fuzzy neural network was applied to recognize individual characters. Our experiment result showed that the proposed recognition algorithm has higher success rate of recognition and faster learning time than the conventional RBF network based recognitions.},
keywords={},
doi={},
ISSN={},
month={June},}
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TY - JOUR
TI - Recognition of English Calling Cards by Using Enhanced Fuzzy Radial Basis Function Neural Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1355
EP - 1362
AU - Kwang-Baek KIM
AU - Young-Ju KIM
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E87-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2004
AB - In this paper, we proposed the novel method for the recognition of English calling cards by using the contour tracking algorithm and the enhanced fuzzy RBF (Radial Basis Function) neural networks. The recognition of calling cards consists of the extraction phase of character areas and the recognition phase of extracted characters. In the extraction phase, first of all, noises are removed from the images of calling cards, and the feature areas including character strings are separated from the calling card images by using the horizontal smearing method and the 8-directional contour tracking method. And using the image projection method the feature areas are split into the areas of individual characters. We also proposed the enhanced fuzzy RBF neural network that organizes the middle layer effectively by using the enhanced fuzzy ART neural network adjusting the vigilance parameter dynamically according to the similarity between patterns. In the recognition phase, the proposed fuzzy neural network was applied to recognize individual characters. Our experiment result showed that the proposed recognition algorithm has higher success rate of recognition and faster learning time than the conventional RBF network based recognitions.
ER -
English
