Unfolder: Fast localization and image rectification of a document with a
  crease from folding in half
A. M. Ershov D. V. Tropin E. E. Limonova D. P. Nikolaev V. V. Arlazarov
Presentation of folded documents is not an uncommon case in modern society.
Digitizing such documents by capturing them with a smartphone camera can be
tricky since a crease can divide the document contents into separate planes. To
unfold the document, one could hold the edges potentially obscuring it in a
captured image. While there are many geometrical rectification methods, they
were usually developed for arbitrary bends and folds. We consider such
algorithms and propose a novel approach Unfolder developed specifically for
images of documents with a crease from folding in half. Unfolder is robust to
projective distortions of the document image and does not fragment the image in
the vicinity of a crease after rectification. A new Folded Document Images
dataset was created to investigate the rectification accuracy of folded (2, 3,
4, and 8 folds) documents. The dataset includes 1600 images captured when
document placed on a table and when held in hand. The Unfolder algorithm
allowed for a recognition error rate of 0.33, which is better than the advanced
neural network methods DocTr (0.44) and DewarpNet (0.57). The average runtime
for Unfolder was only 0.25 s/image on an iPhone XR.