  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.
