Blind bleed-through removal for scanned historical document images with conditional random fields

Due to the quality of paper and long-time preservation, the ink on one side of the historical documents often seeps through and appears on the other side. In this paper, a new blind ink bleed-through removal method is proposed to deal with the scanned historical document images. The scanned historical document image generally consists of three components: foreground, bleed-through and background. In the proposed method, conditional probability distribution (CPD) models of the three components are firstly established by statistics. Then, conditional random fields (CRFs) are used to model the observed scanned image and the corresponding labels. For each input scanned image, parameters of the component-wise CPD models are estimated and belief propagation is performed on the CRFs model to determine the most possible labels. Once the bleed-through component is found, an inpainting algorithm is proposed to remove the ink bleed-through from the input historical image. Experimental results show that the proposed method preserves the foreground component very well and removes the bleed-through effectively.