论文标题
一个封闭式和分叉的堆叠的U-NET模块，用于文档图像露水
A Gated and Bifurcated Stacked U-Net Module for Document Image Dewarping
论文作者
论文摘要
捕获文档的图像是记录它们的最简单，最常用的方法之一。但是，这些图像在手持设备的帮助下被捕获，通常会导致难以消除的不良扭曲。我们提出了一个有监督的门控和分叉的堆叠的U-NET模块，以预测脱水的网格并从输入中创建无失真的图像。尽管网络经过合成扭曲的文档图像培训，但根据现实世界图像计算结果。我们方法中的新颖性不仅存在于U-NET的分叉中，以帮助消除网格坐标的混合，而且还使用了封闭式网络的使用，该网络将边界和其他分钟线级别的细节添加到模型中。美国提出的端到端管道在对先前方法中使用的8％的数据中只有8％的数据进行培训后，在Docunet数据集上实现了最先进的性能。
Capturing images of documents is one of the easiest and most used methods of recording them. These images however, being captured with the help of handheld devices, often lead to undesirable distortions that are hard to remove. We propose a supervised Gated and Bifurcated Stacked U-Net module to predict a dewarping grid and create a distortion free image from the input. While the network is trained on synthetically warped document images, results are calculated on the basis of real world images. The novelty in our methods exists not only in a bifurcation of the U-Net to help eliminate the intermingling of the grid coordinates, but also in the use of a gated network which adds boundary and other minute line level details to the model. The end-to-end pipeline proposed by us achieves state-of-the-art performance on the DocUNet dataset after being trained on just 8 percent of the data used in previous methods.
