Multiview Rectification of Folded Documents
This addresses the need for accurate document scanning and text recognition without expensive 3D scanners, offering a practical solution for digitizing folded documents.
The paper tackles the problem of digitally unwrapping images of curved or folded paper sheets from multiple viewpoints, presenting a method that achieves robust rectification using ridge-aware 3D reconstruction and ℓ₁ conformal mapping, with results demonstrated on various document types like book pages and receipts.
Digitally unwrapping images of paper sheets is crucial for accurate document scanning and text recognition. This paper presents a method for automatically rectifying curved or folded paper sheets from a few images captured from multiple viewpoints. Prior methods either need expensive 3D scanners or model deformable surfaces using over-simplified parametric representations. In contrast, our method uses regular images and is based on general developable surface models that can represent a wide variety of paper deformations. Our main contribution is a new robust rectification method based on ridge-aware 3D reconstruction of a paper sheet and unwrapping the reconstructed surface using properties of developable surfaces via $\ell_1$ conformal mapping. We present results on several examples including book pages, folded letters and shopping receipts.