Query Learning Method for Character Recognition Methods Using Genetic Algorithm

Hitoshi SAKANO

doi:10.1093/ietisy/e88-d.10.2313

Query Learning Method for Character Recognition Methods Using Genetic Algorithm

Hitoshi SAKANO

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We propose a learning method combining query learning and a "genetic translator" we previously developed. Query learning is a useful technique for high-accuracy, high-speed learning and reduction of training sample size. However, it has not been applied to practical optical character readers (OCRs) because human beings cannot recognize queries as character images in the feature space used in practical OCR devices. We previously proposed a character image reconstruction method using a genetic algorithm. This method is applied as a "translator" from feature space for query learning of character recognition. The results of an experiment with hand-written numeral recognition show the possibility of training sample size reduction.

Publication: IEICE TRANSACTIONS on Information Vol.E88-D No.10 pp.2313-2316

Publication Date: 2005/10/01

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DOI: 10.1093/ietisy/e88-d.10.2313

Type of Manuscript: Special Section LETTER (Special Section on Image Recognition and Understanding)

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Hitoshi SAKANO, "Query Learning Method for Character Recognition Methods Using Genetic Algorithm" in IEICE TRANSACTIONS on Information, vol. E88-D, no. 10, pp. 2313-2316, October 2005, doi: 10.1093/ietisy/e88-d.10.2313.
Abstract: We propose a learning method combining query learning and a "genetic translator" we previously developed. Query learning is a useful technique for high-accuracy, high-speed learning and reduction of training sample size. However, it has not been applied to practical optical character readers (OCRs) because human beings cannot recognize queries as character images in the feature space used in practical OCR devices. We previously proposed a character image reconstruction method using a genetic algorithm. This method is applied as a "translator" from feature space for query learning of character recognition. The results of an experiment with hand-written numeral recognition show the possibility of training sample size reduction.
URL: https://globals.ieice.org/en_transactions/information/10.1093/ietisy/e88-d.10.2313/_p

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@ARTICLE{e88-d_10_2313,
author={Hitoshi SAKANO, },
journal={IEICE TRANSACTIONS on Information},
title={Query Learning Method for Character Recognition Methods Using Genetic Algorithm},
year={2005},
volume={E88-D},
number={10},
pages={2313-2316},
abstract={We propose a learning method combining query learning and a "genetic translator" we previously developed. Query learning is a useful technique for high-accuracy, high-speed learning and reduction of training sample size. However, it has not been applied to practical optical character readers (OCRs) because human beings cannot recognize queries as character images in the feature space used in practical OCR devices. We previously proposed a character image reconstruction method using a genetic algorithm. This method is applied as a "translator" from feature space for query learning of character recognition. The results of an experiment with hand-written numeral recognition show the possibility of training sample size reduction.},
keywords={},
doi={10.1093/ietisy/e88-d.10.2313},
ISSN={},
month={October},}

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TY - JOUR
TI - Query Learning Method for Character Recognition Methods Using Genetic Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 2313
EP - 2316
AU - Hitoshi SAKANO
PY - 2005
DO - 10.1093/ietisy/e88-d.10.2313
JO - IEICE TRANSACTIONS on Information
SN -
VL - E88-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2005
AB - We propose a learning method combining query learning and a "genetic translator" we previously developed. Query learning is a useful technique for high-accuracy, high-speed learning and reduction of training sample size. However, it has not been applied to practical optical character readers (OCRs) because human beings cannot recognize queries as character images in the feature space used in practical OCR devices. We previously proposed a character image reconstruction method using a genetic algorithm. This method is applied as a "translator" from feature space for query learning of character recognition. The results of an experiment with hand-written numeral recognition show the possibility of training sample size reduction.
ER -