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    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/55825

    題名: A Binarization Method with Learning-Built Rules for Document Images Produced by Cameras
    作者: Chou, Chien-hsing;Lin, Wen-hsiung;Chang, Fu
    貢獻者: 淡江大學電機工程學系
    關鍵詞: Document imagebinarization;Global threshold;Image processing;Local threshold;Multi-label problem;Non-uniform brightness;Support vectormachine
    日期: 2010-04
    上傳時間: 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
    顯示於類別:[電機工程學系暨研究所] 期刊論文


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