Because of the different types of document degradation such as uneven illumination, image contrast variation, blur caused by humidity, and bleed-through, degraded document image binarization is still an enormous challenge. This paper presents a new binarization method for degraded document images. The proposed algorithm focuses on the differences of image grayscale contrast in different areas. Quadtree is used to divide areas adaptively. In addition, various contrast enhancements are selected to adjust local grayscale contrast in areas with different contrasts. Finally, the local threshold is regarded as the mean of foreground and background gray values, which are determined by the frequency of the gray values. The proposed algorithm was tested on the datasets from the Document Image Binarization Contest (DIBCO) (DIBCO 2009, H-DIBCO 2010, DIBCO 2011, and H-DIBCO 2012). Compared with five other classical algorithms, the images binarized using the proposed algorithm achieved the highest F-measure and peak signal-to-noise ratio and obtained the highest correct rate of recognition.

中文翻译：

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基于对比度增强的退化文档图像二值化

由于文档退化的不同类型，例如照明不均匀，图像对比度变化，由湿度引起的模糊和渗出，退化的文档图像二值化仍然是巨大的挑战。本文提出了一种用于降级文档图像的新二值化方法。该算法针对不同区域图像灰度对比度的差异。四叉树用于自适应地划分区域。另外，选择各种对比度增强以调整具有不同对比度的区域中的局部灰度对比度。最后，将局部阈值视为前景和背景灰度值的平均值，该平均值由灰度值的频率确定。在文档图像二值化竞赛（DIBCO）（DIBCO 2009，H-DIBCO 2010，DIBCO 2011和H-DIBCO 2012）。与其他五种经典算法相比，使用该算法二值化的图像获得了最高F测量和峰值信噪比，并获得最高的正确识别率。