  Document dewarping, aiming to eliminate geometric deformation in photographed
documents to benefit text recognition, has made great progress in recent years
but is still far from being solved. While Cartesian coordinates are typically
leveraged by state-of-the-art approaches to learn a group of deformation
control points, such representation is not efficient for dewarping model to
learn the deformation information. In this work, we explore Polar coordinates
representation for each point in document dewarping, namely Polar-Doc. In
contrast to most current works adopting a two-stage pipeline typically, Polar
representation enables a unified point regression framework for both
segmentation and dewarping network in one single stage. Such unification makes
the whole model more efficient to learn under an end-to-end optimization
pipeline, and also obtains a compact representation. Furthermore, we propose a
novel multi-scope Polar-Doc-IOU loss to constrain the relationship among
control points as a grid-based regularization under the Polar representation.
Visual comparisons and quantitative experiments on two benchmarks show that,
with much fewer parameters than the other mainstream counterparts, our
one-stage model with multi-scope constraints achieves new state-of-the-art
performance on both pixel alignment metrics and OCR metrics. Source codes will
be available at \url{*****}.
