A new type of recurrent neural network for handwritten character recognition

Seong Whan Lee; Young Jaon Kim

doi:10.1109/ICDAR.1995.598939

A new type of recurrent neural network for handwritten character recognition

Seong Whan Lee, Young Jaon Kim

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

17 Citations (Scopus)

Abstract

In this paper, we propose a new type of recurrent neural network for handwritten character recognition. The proposed recurrent neural network differs from Jordan and Elman recurrent neural networks in mew of functions and architectures because it was originally extended from the multilayer feedforward neural network for improving discrimination and generalization power in recognizing handwritten characters. We also analyze the performance of the proposed recurrent neural network by performing recognition experiments with the totally unconstrained handwritten numeral database of Concordia University of Canada. The experimental results showed that the proposed recurrent neural network greatly improves the discrimination and generalization power.

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	38-41
Number of pages	4
ISBN (Electronic)	0818671289
DOIs	https://doi.org/10.1109/ICDAR.1995.598939
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	1
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.1995.598939

Cite this

Lee, S. W., & Kim, Y. J. (1995). A new type of recurrent neural network for handwritten character recognition. In Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995 (pp. 38-41). Article 598939 (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 1). IEEE Computer Society. https://doi.org/10.1109/ICDAR.1995.598939

A new type of recurrent neural network for handwritten character recognition. / Lee, Seong Whan; Kim, Young Jaon.
Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995. IEEE Computer Society, 1995. p. 38-41 598939 (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 1).

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

Lee, SW & Kim, YJ 1995, A new type of recurrent neural network for handwritten character recognition. in Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995., 598939, Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, vol. 1, IEEE Computer Society, pp. 38-41, 3rd International Conference on Document Analysis and Recognition, ICDAR 1995, Montreal, Canada, 95/8/14. https://doi.org/10.1109/ICDAR.1995.598939

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abstract = "In this paper, we propose a new type of recurrent neural network for handwritten character recognition. The proposed recurrent neural network differs from Jordan and Elman recurrent neural networks in mew of functions and architectures because it was originally extended from the multilayer feedforward neural network for improving discrimination and generalization power in recognizing handwritten characters. We also analyze the performance of the proposed recurrent neural network by performing recognition experiments with the totally unconstrained handwritten numeral database of Concordia University of Canada. The experimental results showed that the proposed recurrent neural network greatly improves the discrimination and generalization power.",

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

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",

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AB - In this paper, we propose a new type of recurrent neural network for handwritten character recognition. The proposed recurrent neural network differs from Jordan and Elman recurrent neural networks in mew of functions and architectures because it was originally extended from the multilayer feedforward neural network for improving discrimination and generalization power in recognizing handwritten characters. We also analyze the performance of the proposed recurrent neural network by performing recognition experiments with the totally unconstrained handwritten numeral database of Concordia University of Canada. The experimental results showed that the proposed recurrent neural network greatly improves the discrimination and generalization power.

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A new type of recurrent neural network for handwritten character recognition

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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Cite this