Book image rectification presents unique challenges in document image processing due to complex geometric distortions from binding constraints, where left and right pages exhibit distinctly asymmetric curvature patterns. However, existing single-page document image rectification methods fail to capture the coupled geometric relationships between adjacent pages in books. In this work, we introduce BookNet, the first end-to-end deep learning framework specifically designed for dual-page book image rectification. BookNet adopts a dual-branch architecture with cross-page attention mechanisms, enabling it to estimate warping flows for both individual pages and the complete book spread, explicitly modeling how left and right pages influence each other. Moreover, to address the absence of specialized datasets, we present Book3D, a large-scale synthetic dataset for training, and Book100, a comprehensive real-world benchmark for evaluation. Extensive experiments demonstrate that BookNet outperforms existing state-of-the-art methods on book image rectification. Code and dataset will be made publicly available.

翻译：书籍图像矫正在文档图像处理中面临独特挑战，这是由于装订约束导致的复杂几何畸变，其中左右页面呈现出明显不对称的弯曲模式。然而，现有的单页文档图像矫正方法无法捕捉书籍中相邻页面间的耦合几何关系。本研究提出BookNet——首个专为双页书籍图像矫正设计的端到端深度学习框架。BookNet采用具有跨页注意力机制的双分支架构，使其能够同时估计单页及完整书籍跨页的形变流场，显式建模左右页面间的相互影响机制。此外，为填补专业数据集的空白，我们构建了Book3D（用于训练的大规模合成数据集）和Book100（用于评估的综合性真实场景基准集）。大量实验表明，BookNet在书籍图像矫正任务上优于现有最先进方法。代码与数据集将公开发布。