LAUSR

Document image binarization is the first essential step in digitalizing images and is considered an essential technique in both document image analysis applications and optical character recognition operations, the binarization process is used to obtain a binary image from the original image, binary image is the proper presentation for image segmentation, recognition, and restoration as underlined by several studies which assure that the next step of document image analysis applications depends on the binarization result.  However, old and historical document images mainly suffering from several types of degradations, such as bleeding through the blur, uneven illumination and other types of degradations which makes the binarization process a difficult task. Therefore, extracting of foreground from a degraded background relies on the degradation, furthermore it also depends on the type of used paper and document age. Developed binarization methods are necessary to decrease the impact of the degradation in document background. To resolve this difficulty, this paper proposes an effective, enhanced binarization technique for degraded and historical document images. The proposed method is based on enhancing an existing binarization method by modifying parameters and adding a post-processing stage, thus improving the resulting binary images. This proposed technique is also robust, as there is no need for parameter tuning. After using document image binarization Contest (DIBCO) datasets to evaluate this proposed technique, our findings show that the proposed method efficiency is promising, producing better results than those obtained by some of the winners in the DIBCO.