Presentation 1993/11/12
A Threshold Selection Method for Caracter Images in a Document
Ichiro Nakao, Satoshi Emura, Saki Takakura, Mariko Takenouchi,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Most of Character recognition methods for a document image is applied for binarized images.However,a more effective method is to input a document in gray scale and to select the optimal threshold for individual character areas in the document,thereby improving the precison of character recognition In this paper,we propose a method that selects the threshold for individual character areas by repeating binarization and extraction of character areas from a gray scale document image,and show that this method improves the quality of binarized character images and the precision of character recognition.Furthermore we propose the method to select the threshold for character images by estimating the black pixel ratio of original character fonts,and evaluate by character recognition ratio.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) character recognition / binalization / theshold / character image / document
Paper # AI93-64,PRU93-81
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Conference Information
Committee AI
Conference Date 1993/11/12(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Threshold Selection Method for Caracter Images in a Document
Sub Title (in English)
Keyword(1) character recognition
Keyword(2) binalization
Keyword(3) theshold
Keyword(4) character image
Keyword(5) document
1st Author's Name Ichiro Nakao
1st Author's Affiliation Matsushita Electric Industrial Co.,Ltd.()
2nd Author's Name Satoshi Emura
2nd Author's Affiliation Matsushita Electric Industrial Co.,Ltd.
3rd Author's Name Saki Takakura
3rd Author's Affiliation Matsushita Electric Industrial Co.,Ltd.
4th Author's Name Mariko Takenouchi
4th Author's Affiliation Matsushita Electric Industrial Co.,Ltd.
Date 1993/11/12
Paper # AI93-64,PRU93-81
Volume (vol) vol.93
Number (no) 317
Page pp.pp.-
#Pages 8
Date of Issue