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 always been 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.