  In this work, we propose a new framework, called Document Image Transformer
(DocTr), to address the issue of geometry and illumination distortion of the
document images. Specifically, DocTr consists of a geometric unwarping
transformer and an illumination correction transformer. By setting a set of
learned query embedding, the geometric unwarping transformer captures the
global context of the document image by self-attention mechanism and decodes
the pixel-wise displacement solution to correct the geometric distortion. After
geometric unwarping, our illumination correction transformer further removes
the shading artifacts to improve the visual quality and OCR accuracy. Extensive
evaluations are conducted on several datasets, and superior results are
reported against the state-of-the-art methods. Remarkably, our DocTr achieves
20.02% Character Error Rate (CER), a 15% absolute improvement over the
state-of-the-art methods. Moreover, it also shows high efficiency on running
time and parameter count. The results will be available at
https://github.com/fh2019ustc/DocTr for further comparison.
