文章基本信息

标题：Adaptive Binarization of Unconstrained Hand-Held Camera-Captured Document Images
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作者：Syed Saqib Bukhari (Technical University of Kaiserslautern ; Germany) Faisal Shafait (German Research Center for Artificial Intelligence (DFKI) ; Germany) Thomas M. Breuel (Technical University of Kaiserslautern, Germany) 等
期刊名称：Journal of Universal Computer Science
印刷版ISSN：0948-6968
出版年度：2009
卷号：15
期号：18
出版社：Graz University of Technology and Know-Center
摘要：This paper presents a new adaptive binarization technique for degraded hand-held camera-captured document images. State-of-the-art locally adaptive binarization methods are sensitive to the values of free parameter. This problem is more critical when binarizing degraded camera-captured document images because of distortions like non-uniform illumination, bad shading, blurring, smearing and low resolution. We demonstrate in this paper that local binarization methods are not only sensitive to the selection of free parameters values (either found manually or automatically), but also sensitive to the constant free parameters values for all pixels of a document image. Some range of values of free parameters are better for foreground regions and some other range of values are better for background regions. For overcoming this problem, we present an adaptation of a state-of-the-art local binarization method such that two different set of free parameters values are used for foreground and background regions respectively. We present the use of ridges detection for rough estimation of foreground regions in a document image. This information is then used to calculate appropriate threshold using different set of free parameters values for the foreground and background regions respectively. Evaluation of the method using an OCR-based measure and a pixel-based measure show that our method achieves better performance as compared to state-of-the-art global and local binarization methods.
关键词：binarization, camera-captured document images, document and text processing, image processing and computer vision