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In this paper, a new method for binarization of document images is introduced.  During training, the method stores histograms from training images, along with  the optimal binarization threshold. Training images are presented in pairs, one  noisy version and one clean binarized version, where the latter is used for  finding the optimal binarization threshold.  During use, the method matches the stored histograms to the  histogram for the image that is to be binarized. If a sufficiently close match  is found, the image is binarized using the corresponding threshold associated  with the stored histogram. If no match is found, the contrast of the image is  slightly enhanced, and a new attempt is made. This sequence is repeated until either a match is found,  or a (rare) timeout is reached.  The method has been applied to a set of test images, and has been shown  to outperform several comparable methods.

A Method for Document Image Binarization based on Histogram Matching and Repeated Contrast Enhancement