Binarization of degraded document images based on contrast enhancement

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.