An Improved Adaptive Document Image Binarization Method

Document image binarization is the basis of Optical Character Recognition (OCR). For B. Gatos´ adaptive binarization method exists the shortcomings, we propose an improved adaptive document image binarization method which consists of four steps. The first step is dedicated to a denoising procedure using a low-pass Wiener filter based on local statistics. In the second step, we use a first rough estimation of foreground regions using binarization method based on the Laplacian-Gauss algorithm. As a third step, we compute the background surface of the image by interpolating neighboring background intensities into the foreground areas that result from the previous step. In the fourth step, we proceed to the final binarization by combining information from the calculated background surface and the original image, and accomplish final binary. The method has good robustness for uneven illumination, using the algorithm extract foreground regions, it will get fewer lost strokes and can be effective to retain the edge information. The experimental results show that the improved method has better characteristics than other four kinds of typical document image binarization methods.