Document Image Rectification in Complex Scene Using Stacked Siamese Networks

Zhen Xu, Fei Yin, Peipei Yang, Cheng-Lin Liu

Abstract: With the popularity of digital cameras and smart-phones, capturing document images of physical documents for electronic storage has become popular, but the captured document images suffer various deformations. Document image rectification has been studied intensively, but existing methods do not perform sufficiently for document images captured in complex scenes due to the various environmental factors. In this paper, we propose an end-to-end rectification model by stacking 3D and 2D Siamese networks. Three regularization terms are used to enforce 3D reconstruction consistency and 2D texture consistency, respectively. Experimental results on real world datasets demonstrate that the three regularization terms with Siamese networks can significantly improve the rectification performance, and our method performs superiorly compared to state-of-the-art methods.