  In document image rectification, there exist rich geometric constraints
between the distorted image and the ground truth one. However, such geometric
constraints are largely ignored in existing advanced solutions, which limits
the rectification performance. To this end, we present DocGeoNet for document
image rectification by introducing explicit geometric representation.
Technically, two typical attributes of the document image are involved in the
proposed geometric representation learning, i.e., 3D shape and textlines. Our
motivation arises from the insight that 3D shape provides global unwarping cues
for rectifying a distorted document image while overlooking the local
structure. On the other hand, textlines complementarily provide explicit
geometric constraints for local patterns. The learned geometric representation
effectively bridges the distorted image and the ground truth one. Extensive
experiments show the effectiveness of our framework and demonstrate the
superiority of our DocGeoNet over state-of-the-art methods on both the DocUNet
Benchmark dataset and our proposed DIR300 test set. The code is available at
https://github.com/fh2019ustc/DocGeoNet.
