Abstract

This paper re-visits classical problems in document enhancement. Rather than proposing a new algorithm for a specific problem, we introduce a novel general approach. The key idea is to modify any state-of-the-art algorithm, by providing it with new information (input), improving its own results. Interestingly, this information is based on a solution to a seemingly unrelated problem of visibility detection in R3. We show that a simple representation of an image as a 3D point cloud, gives visibility detection on this cloud a new interpretation. What does it mean for a point to be visible? Although this question has been widely studied within computer vision, it has been always assumed that the point set is a sampling of a real scene. We show that the answer to this question in our context reveals unique and useful information about the image. We demonstrate the benefit of this idea for document binarization and for unshadowing.

Paper

“Document Enhancement using Visibility Detection” [pdf] [Unshadow Visibility Code] [Binarize Visibility Code] [Data]

The code is for academic purposes only.

Please cite this paper if you make use of it:
Document Enhancement using Visibility Detection, Netanel Kligler, Sagi Katz and Ayellet Tal. CVPR 2018