Multiresolution recognition of handwritten numerals with wavelet transform and multilayer cluster neural network

Seong Whan Lee; Young Joon Kim

doi:10.1109/ICDAR.1995.602073

Multiresolution recognition of handwritten numerals with wavelet transform and multilayer cluster neural network

Seong Whan Lee, Young Joon Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

In this paper, we propose a new scheme for multiresolution recognition of totally unconstrained handwritten numerals using wavelet transform and a simple multilayer cluster neural network. The proposed scheme consists of two stages: A feature extraction stage for extracting multiresolution features with wavelet transform, and a classification stage for classifying totally unconstrained handwritten numerals with a simple multilayer cluster neural network. In order to verify the performance of the proposed scheme, experiments with unconstrained handwritten numeral database of Concordia University of Canada, that of Electro-Technical Laboratory of Japan, and that of Electronics and Telecommunications Research Institute of Korea were performed. The error rates were 3.20%, 0.83%, and 0.75%, respectively. These results showed that the proposed scheme is very robust in terms of various writing styles and sizes.

Original language	English
Title of host publication	Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995
Publisher	IEEE Computer Society
Pages	1010-1013
Number of pages	4
ISBN (Electronic)	0818671289
DOIs	https://doi.org/10.1109/ICDAR.1995.602073
Publication status	Published - 1995
Event	3rd International Conference on Document Analysis and Recognition, ICDAR 1995 - Montreal, Canada Duration: 1995 Aug 14 → 1995 Aug 16

Publication series

Name	Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume	2
ISSN (Print)	1520-5363

Conference

Conference	3rd International Conference on Document Analysis and Recognition, ICDAR 1995
Country/Territory	Canada
City	Montreal
Period	95/8/14 → 95/8/16

Bibliographical note

Funding Information:
This research was supported by the Directed Basic Research Fund of Korea Science and Engineering Foundation.

Publisher Copyright:
© 1995 IEEE.

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

Access to Document

10.1109/ICDAR.1995.602073

Cite this

Lee, S. W., & Kim, Y. J. (1995). Multiresolution recognition of handwritten numerals with wavelet transform and multilayer cluster neural network. In Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995 (pp. 1010-1013). Article 602073 (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 2). IEEE Computer Society. https://doi.org/10.1109/ICDAR.1995.602073

Multiresolution recognition of handwritten numerals with wavelet transform and multilayer cluster neural network. / Lee, Seong Whan; Kim, Young Joon.
Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995. IEEE Computer Society, 1995. p. 1010-1013 602073 (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, SW & Kim, YJ 1995, Multiresolution recognition of handwritten numerals with wavelet transform and multilayer cluster neural network. in Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995., 602073, Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, vol. 2, IEEE Computer Society, pp. 1010-1013, 3rd International Conference on Document Analysis and Recognition, ICDAR 1995, Montreal, Canada, 95/8/14. https://doi.org/10.1109/ICDAR.1995.602073

Lee SW, Kim YJ. Multiresolution recognition of handwritten numerals with wavelet transform and multilayer cluster neural network. In Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995. IEEE Computer Society. 1995. p. 1010-1013. 602073. (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR). doi: 10.1109/ICDAR.1995.602073

Lee, Seong Whan ; Kim, Young Joon. / Multiresolution recognition of handwritten numerals with wavelet transform and multilayer cluster neural network. Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995. IEEE Computer Society, 1995. pp. 1010-1013 (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR).

@inproceedings{70fca7a9ab8c44b1bd3c2f6d1470ce88,

title = "Multiresolution recognition of handwritten numerals with wavelet transform and multilayer cluster neural network",

abstract = "In this paper, we propose a new scheme for multiresolution recognition of totally unconstrained handwritten numerals using wavelet transform and a simple multilayer cluster neural network. The proposed scheme consists of two stages: A feature extraction stage for extracting multiresolution features with wavelet transform, and a classification stage for classifying totally unconstrained handwritten numerals with a simple multilayer cluster neural network. In order to verify the performance of the proposed scheme, experiments with unconstrained handwritten numeral database of Concordia University of Canada, that of Electro-Technical Laboratory of Japan, and that of Electronics and Telecommunications Research Institute of Korea were performed. The error rates were 3.20%, 0.83%, and 0.75%, respectively. These results showed that the proposed scheme is very robust in terms of various writing styles and sizes.",

author = "Lee, {Seong Whan} and Kim, {Young Joon}",

note = "Funding Information: This research was supported by the Directed Basic Research Fund of Korea Science and Engineering Foundation. Publisher Copyright: {\textcopyright} 1995 IEEE.; 3rd International Conference on Document Analysis and Recognition, ICDAR 1995 ; Conference date: 14-08-1995 Through 16-08-1995",

year = "1995",

doi = "10.1109/ICDAR.1995.602073",

language = "English",

series = "Proceedings of the International Conference on Document Analysis and Recognition, ICDAR",

publisher = "IEEE Computer Society",

pages = "1010--1013",

booktitle = "Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995",

}

TY - GEN

T1 - Multiresolution recognition of handwritten numerals with wavelet transform and multilayer cluster neural network

AU - Lee, Seong Whan

AU - Kim, Young Joon

PY - 1995

Y1 - 1995

N2 - In this paper, we propose a new scheme for multiresolution recognition of totally unconstrained handwritten numerals using wavelet transform and a simple multilayer cluster neural network. The proposed scheme consists of two stages: A feature extraction stage for extracting multiresolution features with wavelet transform, and a classification stage for classifying totally unconstrained handwritten numerals with a simple multilayer cluster neural network. In order to verify the performance of the proposed scheme, experiments with unconstrained handwritten numeral database of Concordia University of Canada, that of Electro-Technical Laboratory of Japan, and that of Electronics and Telecommunications Research Institute of Korea were performed. The error rates were 3.20%, 0.83%, and 0.75%, respectively. These results showed that the proposed scheme is very robust in terms of various writing styles and sizes.

AB - In this paper, we propose a new scheme for multiresolution recognition of totally unconstrained handwritten numerals using wavelet transform and a simple multilayer cluster neural network. The proposed scheme consists of two stages: A feature extraction stage for extracting multiresolution features with wavelet transform, and a classification stage for classifying totally unconstrained handwritten numerals with a simple multilayer cluster neural network. In order to verify the performance of the proposed scheme, experiments with unconstrained handwritten numeral database of Concordia University of Canada, that of Electro-Technical Laboratory of Japan, and that of Electronics and Telecommunications Research Institute of Korea were performed. The error rates were 3.20%, 0.83%, and 0.75%, respectively. These results showed that the proposed scheme is very robust in terms of various writing styles and sizes.

UR - http://www.scopus.com/inward/record.url?scp=84961717868&partnerID=8YFLogxK

U2 - 10.1109/ICDAR.1995.602073

DO - 10.1109/ICDAR.1995.602073

M3 - Conference contribution

AN - SCOPUS:84961717868

T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

SP - 1010

EP - 1013

BT - Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995

PB - IEEE Computer Society

T2 - 3rd International Conference on Document Analysis and Recognition, ICDAR 1995

Y2 - 14 August 1995 through 16 August 1995

ER -

Multiresolution recognition of handwritten numerals with wavelet transform and multilayer cluster neural network

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this