  Capturing images of documents is one of the easiest and most used methods of
recording them. These images however, being captured with the help of handheld
devices, often lead to undesirable distortions that are hard to remove. We
propose a supervised Gated and Bifurcated Stacked U-Net module to predict a
dewarping grid and create a distortion free image from the input. While the
network is trained on synthetically warped document images, results are
calculated on the basis of real world images. The novelty in our methods exists
not only in a bifurcation of the U-Net to help eliminate the intermingling of
the grid coordinates, but also in the use of a gated network which adds
boundary and other minute line level details to the model. The end-to-end
pipeline proposed by us achieves state-of-the-art performance on the DocUNet
dataset after being trained on just 8 percent of the data used in previous
methods.
