  Compared with flatbed scanners, portable smartphones provide more convenience
for physical document digitization. However, such digitized documents are often
distorted due to uncontrolled physical deformations, camera positions, and
illumination variations. To this end, we present DocScanner, a novel framework
for document image rectification. Different from existing solutions, DocScanner
addresses this issue by introducing a progressive learning mechanism.
Specifically, DocScanner maintains a single estimate of the rectified image,
which is progressively corrected with a recurrent architecture. The iterative
refinements make DocScanner converge to a robust and superior rectification
performance, while the lightweight recurrent architecture ensures the running
efficiency. To further improve the rectification quality, based on the
geometric priori between the distorted and the rectified images, a geometric
regularization is introduced during training to further improve the
performance. Extensive experiments are conducted on the Doc3D dataset and the
DocUNet Benchmark dataset, and the quantitative and qualitative evaluation
results verify the effectiveness of DocScanner, which outperforms previous
methods on OCR accuracy, image similarity, and our proposed distortion metric
by a considerable margin. Furthermore, our DocScanner shows superior efficiency
in runtime latency and model size.
