This paper proposes a novel method for document enhancement which combines two recent powerful noise-reduction steps. The first step is based on the total variation framework. It flattens background grey-levels and produces an intermediate image where background noise is considerably reduced. This image is used as a mask to produce an image with a cleaner background while keeping character details. The second step is applied to the cleaner image and consists of a filter based on non-local means: character edges are smoothed by searching for similar patch images in pixel neighborhoods. The document images to be enhanced are real historical printed documents from several periods which include several defects in their background and on character edges. These defects result from scanning, paper aging and bleed- through. The proposed method enhances document images by combining the total variation and the non-local means techniques in order to improve OCR recognition. The method is shown to be more powerful than when these techniques are used alone and than other enhancement methods.
This is an author-produced, peer-reviewed version of this article. © 2009, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/). The final, definitive version of this document can be found online at Image and Vision Computing, doi: 10.1016/j.imavis.2011.01.001
Likforman-Sulem, Laurence; Darbon, Jérôme; and Barney Smith, Elisa H.. (2011). "Enhancement of Historical Printed Document Images by Combining Total Variation Regularization and Non-Local Means Filtering". Image and Vision Computing, 29(5), 351-363. http://dx.doi.org/10.1016/j.imavis.2011.01.001