  Tremendous efforts have been made on document image rectification, but how to
learn effective representation of such distorted images is still
under-explored. In this paper, we present DocMAE, a novel self-supervised
framework for document image rectification. Our motivation is to encode the
structural cues in document images by leveraging masked autoencoder to benefit
the rectification, i.e., the document boundaries, and text lines. Specifically,
we first mask random patches of the background-excluded document images and
then reconstruct the missing pixels. With such a self-supervised learning
approach, the network is encouraged to learn the intrinsic structure of
deformed documents by restoring document boundaries and missing text lines.
Transfer performance in the downstream rectification task validates the
effectiveness of our method. Extensive experiments are conducted to demonstrate
the effectiveness of our method.
