  In recent years, tremendous efforts have been made on document image
rectification, but existing advanced algorithms are limited to processing
restricted document images, i.e., the input images must incorporate a complete
document. Once the captured image merely involves a local text region, its
rectification quality is degraded and unsatisfactory. Our previously proposed
DocTr, a transformer-assisted network for document image rectification, also
suffers from this limitation. In this work, we present DocTr++, a novel unified
framework for document image rectification, without any restrictions on the
input distorted images. Our major technical improvements can be concluded in
three aspects. Firstly, we upgrade the original architecture by adopting a
hierarchical encoder-decoder structure for multi-scale representation
extraction and parsing. Secondly, we reformulate the pixel-wise mapping
relationship between the unrestricted distorted document images and the
distortion-free counterparts. The obtained data is used to train our DocTr++
for unrestricted document image rectification. Thirdly, we contribute a
real-world test set and metrics applicable for evaluating the rectification
quality. To our best knowledge, this is the first learning-based method for the
rectification of unrestricted document images. Extensive experiments are
conducted, and the results demonstrate the effectiveness and superiority of our
method. We hope our DocTr++ will serve as a strong baseline for generic
document image rectification, prompting the further advancement and application
of learning-based algorithms. The source code and the proposed dataset are
publicly available at https://github.com/fh2019ustc/DocTr-Plus.
