Layout is a fundamental component of any graphic design. Creating large
varieties of plausible document layouts can be a tedious task, requiring
numerous constraints to be satisfied, including local ones relating different
semantic elements and global constraints on the general appearance and spacing.
In this paper, we present a novel framework, coined READ, for REcursive
Autoencoders for Document layout generation, to generate plausible 2D layouts
of documents in large quantities and varieties. First, we devise an exploratory
recursive method to extract a structural decomposition of a single document.
Leveraging a dataset of documents annotated with labeled bounding boxes, our
recursive neural network learns to map the structural representation, given in
the form of a simple hierarchy, to a compact code, the space of which is
approximated by a Gaussian distribution. Novel hierarchies can be sampled from
this space, obtaining new document layouts. Moreover, we introduce a
combinatorial metric to measure structural similarity among document layouts.
We deploy it to show that our method is able to generate highly variable and
realistic layouts. We further demonstrate the utility of our generated layouts
in the context of standard detection tasks on documents, showing that detection
performance improves when the training data is augmented with generated
documents whose layouts are produced by READ.

本文提出了一种名为 READ 的新框架，利用递归自动编码器生成大量和多样的文档 2D 布局，其中采用了一种递归的方法来提取文档的结构分解，并利用标注边界框的文档数据集对此进行了学习，从而获得了结构表示形式，并将其映射到高斯空间，从而生成新的文本布局，同时，还引入了一种组合度量来测量文档布局之间的结构相似性，并证明了所生成文档布局的高可变性和实际性，在文本检测任务中证明了所生成布局的作用。