Multi-lingual, multi-font and multi-size large-set character recognition using self-organizing neural network

Seong Whan Lee; Jong Soo Kim

doi:10.1109/ICDAR.1995.598937

Multi-lingual, multi-font and multi-size large-set character recognition using self-organizing neural network

Seong Whan Lee, Jong Soo Kim

Department of Brain and Cognitive Engineering

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

11 Citations (Scopus)

Abstract

In this paper, we propose a practical scheme for multi-lingual) multi-font, and multi-size large-set character recognition using self-organizing neural network. In order to improve the performance of the proposed scheme, a nonlinear shape normalization based on dot density and three kinds of hierarchical features are introduced. For coarse classification, two kinds of classifiers are proposed. One is a hierarchical tree classifier, and the other is a SOFM/LVQ based classifier which is composed of an adaptive SOFM coarseclassifier and LVQ4 language-classifiers. For fine classification, an LVQ4 classifier has been adopted. In order to evaluate the performance of the proposed scheme, recognition experiments with 3,367,200 characters having 7,320 diflerent classes have been carried out on a 486 DX-2 66MHz PC. Experimental results reveal that the proposed scheme using an adaptive SOFM coarse-classifier, LVQ4 languageclassifiers, and LVQ4 fine-classifiers has high recognition rate of over 98.27% and fast execution time of more than 40 characters per second.

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	28-33
Number of pages	6
ISBN (Electronic)	0818671289
DOIs	https://doi.org/10.1109/ICDAR.1995.598937
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:
Classification Method 2 Method 1 This research was supported by the POSDATA Co.

Publisher Copyright:
© 1995 IEEE.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

Access to Document

10.1109/ICDAR.1995.598937

Cite this

Lee, S. W., & Kim, J. S. (1995). Multi-lingual, multi-font and multi-size large-set character recognition using self-organizing neural network. In Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995 (pp. 28-33). Article 598937 (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 1). IEEE Computer Society. https://doi.org/10.1109/ICDAR.1995.598937

Multi-lingual, multi-font and multi-size large-set character recognition using self-organizing neural network. / Lee, Seong Whan; Kim, Jong Soo.
Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995. IEEE Computer Society, 1995. p. 28-33 598937 (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, JS 1995, Multi-lingual, multi-font and multi-size large-set character recognition using self-organizing neural network. in Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995., 598937, Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, vol. 1, IEEE Computer Society, pp. 28-33, 3rd International Conference on Document Analysis and Recognition, ICDAR 1995, Montreal, Canada, 95/8/14. https://doi.org/10.1109/ICDAR.1995.598937

Lee SW, Kim JS. Multi-lingual, multi-font and multi-size large-set character recognition using self-organizing neural network. In Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995. IEEE Computer Society. 1995. p. 28-33. 598937. (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR). doi: 10.1109/ICDAR.1995.598937

Lee, Seong Whan ; Kim, Jong Soo. / Multi-lingual, multi-font and multi-size large-set character recognition using self-organizing neural network. Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995. IEEE Computer Society, 1995. pp. 28-33 (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR).

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abstract = "In this paper, we propose a practical scheme for multi-lingual) multi-font, and multi-size large-set character recognition using self-organizing neural network. In order to improve the performance of the proposed scheme, a nonlinear shape normalization based on dot density and three kinds of hierarchical features are introduced. For coarse classification, two kinds of classifiers are proposed. One is a hierarchical tree classifier, and the other is a SOFM/LVQ based classifier which is composed of an adaptive SOFM coarseclassifier and LVQ4 language-classifiers. For fine classification, an LVQ4 classifier has been adopted. In order to evaluate the performance of the proposed scheme, recognition experiments with 3,367,200 characters having 7,320 diflerent classes have been carried out on a 486 DX-2 66MHz PC. Experimental results reveal that the proposed scheme using an adaptive SOFM coarse-classifier, LVQ4 languageclassifiers, and LVQ4 fine-classifiers has high recognition rate of over 98.27% and fast execution time of more than 40 characters per second.",

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N2 - In this paper, we propose a practical scheme for multi-lingual) multi-font, and multi-size large-set character recognition using self-organizing neural network. In order to improve the performance of the proposed scheme, a nonlinear shape normalization based on dot density and three kinds of hierarchical features are introduced. For coarse classification, two kinds of classifiers are proposed. One is a hierarchical tree classifier, and the other is a SOFM/LVQ based classifier which is composed of an adaptive SOFM coarseclassifier and LVQ4 language-classifiers. For fine classification, an LVQ4 classifier has been adopted. In order to evaluate the performance of the proposed scheme, recognition experiments with 3,367,200 characters having 7,320 diflerent classes have been carried out on a 486 DX-2 66MHz PC. Experimental results reveal that the proposed scheme using an adaptive SOFM coarse-classifier, LVQ4 languageclassifiers, and LVQ4 fine-classifiers has high recognition rate of over 98.27% and fast execution time of more than 40 characters per second.

AB - In this paper, we propose a practical scheme for multi-lingual) multi-font, and multi-size large-set character recognition using self-organizing neural network. In order to improve the performance of the proposed scheme, a nonlinear shape normalization based on dot density and three kinds of hierarchical features are introduced. For coarse classification, two kinds of classifiers are proposed. One is a hierarchical tree classifier, and the other is a SOFM/LVQ based classifier which is composed of an adaptive SOFM coarseclassifier and LVQ4 language-classifiers. For fine classification, an LVQ4 classifier has been adopted. In order to evaluate the performance of the proposed scheme, recognition experiments with 3,367,200 characters having 7,320 diflerent classes have been carried out on a 486 DX-2 66MHz PC. Experimental results reveal that the proposed scheme using an adaptive SOFM coarse-classifier, LVQ4 languageclassifiers, and LVQ4 fine-classifiers has high recognition rate of over 98.27% and fast execution time of more than 40 characters per second.

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Multi-lingual, multi-font and multi-size large-set character recognition using self-organizing neural network

Abstract

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Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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