Large ground-truth datasets and recent advances in deep learning techniques
have been useful for layout detection. However, because of the restricted
layout diversity of these datasets, training on them requires a sizable number
of annotated instances, which is both expensive and time-consuming. As a
result, differences between the source and target domains may significantly
impact how well these models function. To solve this problem, domain adaptation
approaches have been developed that use a small quantity of labeled data to
adjust the model to the target domain. In this research, we introduced a
synthetic document dataset called RanLayNet, enriched with automatically
assigned labels denoting spatial positions, ranges, and types of layout
elements. The primary aim of this endeavor is to develop a versatile dataset
capable of training models with robustness and adaptability to diverse document
formats. Through empirical experimentation, we demonstrate that a deep layout
identification model trained on our dataset exhibits enhanced performance
compared to a model trained solely on actual documents. Moreover, we conduct a
comparative analysis by fine-tuning inference models using both PubLayNet and
IIIT-AR-13K datasets on the Doclaynet dataset. Our findings emphasize that
models enriched with our dataset are optimal for tasks such as achieving 0.398
and 0.588 mAP95 score in the scientific document domain for the TABLE class.

为了解决有限多样性数据集对深度学习模型的影响问题，我们引入了一个包含空间位置、范围和布局元素类型的标签的合成文档数据集 RanLayNet。经过实验证明，使用我们的数据集训练的深度布局识别模型相比仅使用实际文档训练的模型表现更优。此外，我们通过对 Doclaynet 数据集使用 PubLayNet 和 IIIT-AR-13K 数据集的微调模型进行比较分析，发现使用我们的数据集进行增强的模型在科学文档领域的 TABLE 类任务中取得了 0.398 和 0.588 的 mAP95 分数。

RanLayNet：一种用于领域自适应和泛化的文档布局检测数据集

RanLayNet: A Dataset for Document Layout Detection used for Domain  Adaptation and Generalization

Recently there is an increasing interest in scene generation within the
research community. However, models used for generating scene layouts from
textual description largely ignore plausible visual variations within the
structure dictated by the text. We propose LayoutVAE, a variational autoencoder
based framework for generating stochastic scene layouts. LayoutVAE is a
versatile modeling framework that allows for generating full image layouts
given a label set, or per label layouts for an existing image given a new
label. In addition, it is also capable of detecting unusual layouts,
potentially providing a way to evaluate layout generation problem. Extensive
experiments on MNIST-Layouts and challenging COCO 2017 Panoptic dataset
verifies the effectiveness of our proposed framework.

LayoutVAE 是一种基于变分自编码器的场景布局生成框架，它可以根据文本或标签生成场景布局，检测布局异常问题，并在 MNIST-Layouts 和 COCO 2017 Panoptic 数据集上取得了良好的效果。