  With the advent of mobile and hand-held cameras, document images have found
their way into almost every domain. Dewarping of these images for the removal
of perspective distortions and folds is essential so that they can be
understood by document recognition algorithms. For this, we propose an
end-to-end CNN architecture that can produce distortion free document images
from warped documents it takes as input. We train this model on warped document
images simulated synthetically to compensate for lack of enough natural data.
Our method is novel in the use of a bifurcated decoder with shared weights to
prevent intermingling of grid coordinates, in the use of residual networks in
the U-Net skip connections to allow flow of data from different receptive
fields in the model, and in the use of a gated network to help the model focus
on structure and line level detail of the document image. We evaluate our
method on the DocUNet dataset, a benchmark in this domain, and obtain results
comparable to state-of-the-art methods.
