A Binarization Method with Learning-Built Rules for Document Images Produced by Cameras

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 期刊論文 > Item 987654321/55825

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/55825

题名:	A Binarization Method with Learning-Built Rules for Document Images Produced by Cameras
作者:	Chou, Chien-hsing;Lin, Wen-hsiung;Chang, Fu
贡献者:	淡江大學電機工程學系
关键词:	Document imagebinarization;Global threshold;Image processing;Local threshold;Multi-label problem;Non-uniform brightness;Support vectormachine
日期:	2010-04
上传时间:	2011-08-28 16:34:34 (UTC+8)
出版者:	Kidlington: Pergamon
摘要:	In this paper, we propose a novel binarization method for document images produced by cameras. Such images often have varying degrees of brightness and require more careful treatment than merely applying a statistical method to obtain a threshold value. To resolve the problem, the proposed method divides an image into several regions and decides how to binarize each region. The decision rules are derived from a learning process that takes training images as input. Tests on images produced under normal and inadequate illumination conditions show that our method yields better visual quality and better OCR performance than three global binarization methods and four locally adaptive binarization methods.
關聯:	Pattern Recognition 43(4), pp.1518-1530
DOI:	10.1016/j.patcog.2009.10.016
显示于类别:	[電機工程學系暨研究所] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
0031-3203_43(4)_p1518-1530.pdf		1759Kb	Adobe PDF	352	检视/开启
index.html		0Kb	HTML	296	检视/开启

在機構典藏中所有的数据项都受到原著作权保护.

TAIR相关文章