  State-of-the-art document dewarping techniques learn to predict 3-dimensional
information of documents which are prone to errors while dealing with documents
with irregular distortions or large variations in depth. This paper presents
FDRNet, a Fourier Document Restoration Network that can restore documents with
different distortions and improve document recognition in a reliable and
simpler manner. FDRNet focuses on high-frequency components in the Fourier
space that capture most structural information but are largely free of
degradation in appearance. It dewarps documents by a flexible Thin-Plate Spline
transformation which can handle various deformations effectively without
requiring deformation annotations in training. These features allow FDRNet to
learn from a small amount of simply labeled training images, and the learned
model can dewarp documents with complex geometric distortion and recognize the
restored texts accurately. To facilitate document restoration research, we
create a benchmark dataset consisting of over one thousand camera documents
with different types of geometric and photometric distortion. Extensive
experiments show that FDRNet outperforms the state-of-the-art by large margins
on both dewarping and text recognition tasks. In addition, FDRNet requires a
small amount of simply labeled training data and is easy to deploy.
