One of the major prerequisites for any deep learning approach is the availability of large-scale training data. When dealing with scanned document images in real world scenarios, the principal information of its content is stored in the layout itself. In this work, we have proposed an automated deep generative model using Graph Neural Networks (GNNs) to generate synthetic data with highly variable and plausible document layouts that can be used to train document interpretation systems, in this case, specially in digital mailroom applications. It is also the first graph-based approach for document layout generation task experimented on administrative document images, in this case, invoices.

翻译：任何深层学习方法的主要先决条件之一是提供大规模培训数据。在处理现实世界情景中扫描文件图像时,其主要内容信息储存在版式本身中。在这项工作中,我们提议采用一个自动深层基因模型,使用图形神经网络(GNNS)生成合成数据,其文件布局极易变和可信的文件布局,可用于培训文件解释系统,特别是数字邮件室应用程序,这也是在行政文件图像上实验的文件布局生成任务中的第一个基于图表的方法,在本案中是发票。