利用文档特征线对齐特性，提升去畸变效果
Axis-Aligned Document Dewarping
- 以轴对齐几何约束增强训练，利用文档固有结构
- 推理前预处理实现轴对齐，降低去畸变难度
- 提出新评估指标AAD，更贴合人眼感知且更鲁棒
文档去畸变在诸多应用中至关重要。现有基于学习的方法高度依赖带标注数据的监督回归，未能充分挖掘物理文档的内在几何特性。本文核心洞察是：良好去畸变的文档由其轴对齐的特征线定义，这与平面文档离散网格几何的轴对齐本质一致。据此，我们提出三项协同贡献：训练阶段引入轴对齐几何约束以增强去畸变；推理阶段设计轴对齐预处理策略以降低任务难度；评估阶段提出新指标轴对齐失真（AAD），兼具几何意义、符合人眼感知且更具鲁棒性。实验表明，该方法在多个基准上达到领先性能，AAD指标提升18.2%至34.5%。代码已开源：https://github.com/chaoyunwang/AADD。
原文摘要 · Abstract (English)
Document dewarping is crucial for many applications. However, existing learning-based methods rely heavily on supervised regression with annotated data without fully leveraging the inherent geometric properties of physical documents. Our key insight is that a well-dewarped document is defined by its axis-aligned feature lines. This property aligns with the inherent axis-aligned nature of the discrete grid geometry in planar documents. Harnessing this property, we introduce three synergistic contributions: for the training phase, we propose an axis-aligned geometric constraint to enhance document dewarping; for the inference phase, we propose an axis alignment preprocessing strategy to reduce the dewarping difficulty; and for the evaluation phase, we introduce a new metric, Axis-Aligned Distortion (AAD), that not only incorporates geometric meaning and aligns with human visual perception but also demonstrates greater robustness. As a result, our method achieves state-of-the-art performance on multiple existing benchmarks, improving the AAD metric by 18.2% to 34.5%. The code is publicly available at https://github.com/chaoyunwang/AADD.
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