A binarization method with learning-built rules for document images produced by cameras”,in: proceeding of ELSEVIER,2009,pp. 1518–1530

In this paper, we propose a novel binarization method for document images pro-duced by cameras. Such images often have varying degrees of brightness and re-quire more careful treatment than merely applying a statistical method to obtain a threshold value. To resolve the problem, our method divides an image into several regions and decides how to binarize each region. The decision rules are derived from a learning process that takes training images as input. Tests on images pro-duced under normal and inadequate illumination conditions show that our method yields better visual quality and better OCR performance than three global binari-zation methods and four locally adaptive binarization methods