Fuzzy and crisp handwritten character recognition using neural networks

Paul Gader, Magdi Mohamed, Jung-Hsien Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationIntelligent Engineering Systems Through Artificial Neural Networks
EditorsC.H. Dagli, L.I. Burke, Y.C. Shin
PublisherASME
Pages421-426
Number of pages6
Volume2
Publication statusPublished - 1992
EventProceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92 - St.Louis, MO, USA
Duration: 1992 Nov 151992 Nov 18

Other

OtherProceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92
CitySt.Louis, MO, USA
Period92-11-1592-11-18

Fingerprint

Character recognition
Neural networks
Fuzzy neural networks
Backpropagation

All Science Journal Classification (ASJC) codes

  • Software

Cite this

Gader, P., Mohamed, M., & Chiang, J-H. (1992). Fuzzy and crisp handwritten character recognition using neural networks. In C. H. Dagli, L. I. Burke, & Y. C. Shin (Eds.), Intelligent Engineering Systems Through Artificial Neural Networks (Vol. 2, pp. 421-426). ASME.
Gader, Paul ; Mohamed, Magdi ; Chiang, Jung-Hsien. / Fuzzy and crisp handwritten character recognition using neural networks. Intelligent Engineering Systems Through Artificial Neural Networks. editor / C.H. Dagli ; L.I. Burke ; Y.C. Shin. Vol. 2 ASME, 1992. pp. 421-426
@inproceedings{5176b8b602114a5796c298ea6483202a,
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 Jung-Hsien Chiang",
year = "1992",
language = "English",
volume = "2",
pages = "421--426",
editor = "C.H. Dagli and L.I. Burke and Y.C. Shin",
booktitle = "Intelligent Engineering Systems Through Artificial Neural Networks",
publisher = "ASME",

}

Gader, P, Mohamed, M & Chiang, J-H 1992, Fuzzy and crisp handwritten character recognition using neural networks. in CH Dagli, LI Burke & YC Shin (eds), Intelligent Engineering Systems Through Artificial Neural Networks. vol. 2, ASME, pp. 421-426, Proceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92, St.Louis, MO, USA, 92-11-15.

Fuzzy and crisp handwritten character recognition using neural networks. / Gader, Paul; Mohamed, Magdi; Chiang, Jung-Hsien.

Intelligent Engineering Systems Through Artificial Neural Networks. ed. / C.H. Dagli; L.I. Burke; Y.C. Shin. Vol. 2 ASME, 1992. p. 421-426.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Fuzzy and crisp handwritten character recognition using neural networks

AU - Gader, Paul

AU - Mohamed, Magdi

AU - Chiang, Jung-Hsien

PY - 1992

Y1 - 1992

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.

UR - http://www.scopus.com/inward/record.url?scp=0027105377&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0027105377&partnerID=8YFLogxK

M3 - Conference contribution

VL - 2

SP - 421

EP - 426

BT - Intelligent Engineering Systems Through Artificial Neural Networks

A2 - Dagli, C.H.

A2 - Burke, L.I.

A2 - Shin, Y.C.

PB - ASME

ER -

Gader P, Mohamed M, Chiang J-H. Fuzzy and crisp handwritten character recognition using neural networks. In Dagli CH, Burke LI, Shin YC, editors, Intelligent Engineering Systems Through Artificial Neural Networks. Vol. 2. ASME. 1992. p. 421-426