Presentation 1999/7/16
Recognition of handwritten characters: a comparison between human reader and OCR using neural networks
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) In recent years the recognition of handwritten characters by OCR has been reported to mark as high as 98 -99% of accuracy rate. These results were obtained for the characters written in specific boxes or frames carefully. Such a high accuracy rate cannot be expected, however, for broken characters that result from rough printing. In the present paper we compared human readers' recognition ability for poorly handwritten or similar characters with the ability of the character recognition system using neural network (ELNET-II),for two handwritten character databases (ETL9B and IPTP). The experiments showed that human recognition ability was better, in comparison with OCR, especially in recognition of those characters that were broken or written in a cursive style. We can consider that the results depend on the difference of the processing ability of the local fluctuation of character strokes.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) handwritten character recognition / neural networks / poorly handwritten character / similar character
Paper # HIP99-22
Date of Issue

Conference Information
Committee HIP
Conference Date 1999/7/16(1days)
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Place (in English)
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Paper Information
Registration To Human Information Processing (HIP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Recognition of handwritten characters: a comparison between human reader and OCR using neural networks
Sub Title (in English)
Keyword(1) handwritten character recognition
Keyword(2) neural networks
Keyword(3) poorly handwritten character
Keyword(4) similar character
1st Author's Name Kazuki SARUTA
1st Author's Affiliation Faculty of Systems Science and Technology, Akita Prefectural University()
2nd Author's Name Yoichi WATANABE
2nd Author's Affiliation Faculty of Literature and Social Sciences, Yamagata University
Date 1999/7/16
Paper # HIP99-22
Volume (vol) vol.99
Number (no) 186
Page pp.pp.-
#Pages 6
Date of Issue