Fuzzy and crisp handwritten character recognition using neural networks

Paul Gader; Magdi Mohamed; Jung Hsien Chiang

Fuzzy and crisp handwritten character recognition using neural networks

Paul Gader, Magdi Mohamed, Jung Hsien Chiang

Department of Computer Science and Information Engineering

Research output: Contribution to conference › Paper › peer-review

5 Citations (Scopus)

Abstract

In this paper, we describe a neural-network-based handwritten alphabetic character recognition system. Feature vectors are generated using a two-pass algorithm to generate direction magnitude images followed by summing over overlapping zones. Crisp neural networks and fuzzy neural networks are trained using back-propagation. In the crisp case, the desired output is high for the correct class and low for all others. In the fuzzy case, the desired outputs are set using a fuzzy k-nearest neighbor algorithm.

Original language	English
Pages	421-426
Number of pages	6
Publication status	Published - 1992 Dec 1
Event	Proceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92 - St.Louis, MO, USA Duration: 1992 Nov 15 → 1992 Nov 18

Other

Other	Proceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92
City	St.Louis, MO, USA
Period	92-11-15 → 92-11-18

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Cite this

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title = "Fuzzy and crisp handwritten character recognition using neural networks",

abstract = "In this paper, we describe a neural-network-based handwritten alphabetic character recognition system. Feature vectors are generated using a two-pass algorithm to generate direction magnitude images followed by summing over overlapping zones. Crisp neural networks and fuzzy neural networks are trained using back-propagation. In the crisp case, the desired output is high for the correct class and low for all others. In the fuzzy case, the desired outputs are set using a fuzzy k-nearest neighbor algorithm.",

author = "Paul Gader and Magdi Mohamed and Chiang, {Jung Hsien}",

year = "1992",

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AU - Gader, Paul

AU - Mohamed, Magdi

AU - Chiang, Jung Hsien

PY - 1992/12/1

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N2 - In this paper, we describe a neural-network-based handwritten alphabetic character recognition system. Feature vectors are generated using a two-pass algorithm to generate direction magnitude images followed by summing over overlapping zones. Crisp neural networks and fuzzy neural networks are trained using back-propagation. In the crisp case, the desired output is high for the correct class and low for all others. In the fuzzy case, the desired outputs are set using a fuzzy k-nearest neighbor algorithm.

AB - In this paper, we describe a neural-network-based handwritten alphabetic character recognition system. Feature vectors are generated using a two-pass algorithm to generate direction magnitude images followed by summing over overlapping zones. Crisp neural networks and fuzzy neural networks are trained using back-propagation. In the crisp case, the desired output is high for the correct class and low for all others. In the fuzzy case, the desired outputs are set using a fuzzy k-nearest neighbor algorithm.

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M3 - Paper

AN - SCOPUS:0027105377

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T2 - Proceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92

Y2 - 15 November 1992 through 18 November 1992

ER -

Fuzzy and crisp handwritten character recognition using neural networks

Abstract

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