A learning framework for the optimization and automation of document binarization methods
Computer Vision and Image Understanding
Historical document binarization based on phase information of images
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
A combined approach for the binarization of handwritten document images
Pattern Recognition Letters
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Document image binarization has been studied for decades, and many practical binarization techniques have been proposed for different kinds of document images. However, many state-of-the-art methods are particularly suitable for the document images that suffer from certain specific type of image degradation or have certain specific type of image characteristics. In this paper, we propose a classification framework to combine different thresholding methods and produce better performance for document image binarization. Given the binarization results of some reported methods, the proposed framework divides the document image pixels into three sets, namely, foreground pixels, background pixels and uncertain pixels. A classifier is then applied to iteratively classify those uncertain pixels into foreground and background, based on the pre-selected froeground and background sets. Extensive experiments over different datasets including the Document Image Binarization Contest(DIBCO)2009 and Handwritten Document Image Binarization Competition(H-DIBCO)2010 show that our proposed framework outperforms most state-of-the-art methods significantly.