Document Bleed-Through Removal Using Sparse Image Inpainting

Bleed-through is a pervasive degradation in ancient documents, caused by the ink of the opposite side of the sheet that has seeped through the paper fiber, and appears as an extra, interfering text. Bleed-through severely impairs document readability and makes it difficult to decipher the contents. Digital image restoration techniques have been successfully employed to remove or significantly reduce this distortion. The main theme is to identify the bleedthrough pixels and estimate an appropriate replacement for them, in accordance to their surrounding. This paper proposes a two-step image restoration method, exploiting information from the recto and verso images. First, based on a non-stationary linear model of the two texts overlapped in the recto-verso pair, the bleed through pixels are identified. In the second step, a sparse representation based image inpainting technique, with a non-negative sparsity constraint, is used to find an appropriate replacement for the bleedthough pixels. Thanks to the power of dictionary learning and sparse image reconstruction methods, the natural texture of the background is well reproduced in the bleed-through areas, and even a their possible overestimation is effectively corrected, so that the original appearance of the document is preserved. The experiments are conducted on the images of a popular database of ancient documents, and the results validate the performance of the proposed method compared to the state of the art.