Document dewarping from a distorted camera-captured image is of great value
for OCR and document understanding. The document boundary plays an important
role which is more evident than the inner region in document dewarping. Current
learning-based methods mainly focus on complete boundary cases, leading to poor
document correction performance of documents with incomplete boundaries. In
contrast to these methods, this paper proposes MataDoc, the first method
focusing on arbitrary boundary document dewarping with margin and text aware
regularizations. Specifically, we design the margin regularization by
explicitly considering background consistency to enhance boundary perception.
Moreover, we introduce word position consistency to keep text lines straight in
rectified document images. To produce a comprehensive evaluation of MataDoc, we
propose a novel benchmark ArbDoc, mainly consisting of document images with
arbitrary boundaries in four typical scenarios. Extensive experiments confirm
the superiority of MataDoc with consideration for the incomplete boundary on
ArbDoc and also demonstrate the effectiveness of the proposed method on
DocUNet, DIR300, and WarpDoc datasets.

本文提出了 MataDoc 方法，首次专注于具有边界矩形和文本感知正则化的任意边界文档矫正，通过明确考虑背景一致性来增强边界感知，引入了词位置一致性以保持矫正后的文档图像中的文本行直线，通过 ArbDoc 基准测试全面评估了 MataDoc 方法，在 ArbDoc、DocUNet、DIR300 和 WarpDoc 数据集上证明了该方法的优越性。