基于几何表示学习的文档图像校正

Hao Feng, Wen-gang Zhou, Jiajun Deng, Yuechen Wang, Houqiang Li
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引用次数: 10

摘要

在文档图像校正中,失真图像与地面真实图像之间存在着丰富的几何约束。然而,在现有的先进解决方案中,这种几何约束在很大程度上被忽略,从而限制了整流性能。为此,我们通过引入显式几何表示,提出了用于文档图像校正的DocGeoNet。从技术上讲,所提出的几何表示学习涉及文档图像的两个典型属性,即3D形状和文本线。我们的动机源于这样一种见解,即3D形状为纠正扭曲的文档图像提供了全局解扭曲线索,同时忽略了局部结构。另一方面,文本线补充地为局部模式提供明确的几何约束。学习到的几何表示有效地连接了扭曲图像和真实图像。大量的实验表明了我们的框架的有效性,并证明了我们的DocGeoNet在DocUNet基准数据集和我们提出的DIR300测试集上优于最先进的方法。代码可在https://github.com/fh2019ustc/DocGeoNet上获得。
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Geometric Representation Learning for Document Image Rectification
In document image rectification, there exist rich geometric constraints between the distorted image and the ground truth one. However, such geometric constraints are largely ignored in existing advanced solutions, which limits the rectification performance. To this end, we present DocGeoNet for document image rectification by introducing explicit geometric representation. Technically, two typical attributes of the document image are involved in the proposed geometric representation learning, i.e., 3D shape and textlines. Our motivation arises from the insight that 3D shape provides global unwarping cues for rectifying a distorted document image while overlooking the local structure. On the other hand, textlines complementarily provide explicit geometric constraints for local patterns. The learned geometric representation effectively bridges the distorted image and the ground truth one. Extensive experiments show the effectiveness of our framework and demonstrate the superiority of our DocGeoNet over state-of-the-art methods on both the DocUNet Benchmark dataset and our proposed DIR300 test set. The code is available at https://github.com/fh2019ustc/DocGeoNet.
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