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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/55825

    Title: A Binarization Method with Learning-Built Rules for Document Images Produced by Cameras
    Authors: Chou, Chien-hsing;Lin, Wen-hsiung;Chang, Fu
    Contributors: 淡江大學電機工程學系
    Keywords: Document imagebinarization;Global threshold;Image processing;Local threshold;Multi-label problem;Non-uniform brightness;Support vectormachine
    Date: 2010-04
    Issue Date: 2011-08-28 16:34:34 (UTC+8)
    Publisher: Kidlington: Pergamon
    Abstract: 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.
    Relation: Pattern Recognition 43(4), pp.1518-1530
    DOI: 10.1016/j.patcog.2009.10.016
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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