Document layout analysis is a known problem to the documents research community and has been vastly explored yielding a multitude of solutions ranging from text mining, and recognition to graph-based representation, visual feature extraction, \emph{etc}. However, most of the existing works have ignored the crucial fact regarding the scarcity of labeled data. With growing internet connectivity to personal life, an enormous amount of documents had been available in the public domain and thus making data annotation a tedious task. We address this challenge using self-supervision and unlike, the few existing self-supervised document segmentation approaches which use text mining and textual labels, we use a complete vision-based approach in pre-training without any ground-truth label or its derivative. Instead, we generate pseudo-layouts from the document images to pre-train an image encoder to learn the document object representation and localization in a self-supervised framework before fine-tuning it with an object detection model. We show that our pipeline sets a new benchmark in this context and performs at par with the existing methods and the supervised counterparts, if not outperforms. The code is made publicly available at: \href{https://github.com/MaitySubhajit/SelfDocSeg}{github.com/MaitySubhajit/SelfDocSeg

翻译：文档布局分析是文档研究领域的一个已知问题，并已被广泛探索，产生了多种解决方案，包括文本挖掘、识别、基于图的表示、视觉特征提取等。然而，现有的大多数工作忽略了关于标注数据稀缺的关键事实。随着互联网与个人生活的日益连接，大量文档已在公共领域可用，从而使数据标注成为一项繁琐的任务。我们利用自监督来应对这一挑战，与少数现有的利用文本挖掘和文本标签的自监督文档分割方法不同，我们在预训练中使用完全基于视觉的方法，无需任何真实标签或其衍生信息。相反，我们从文档图像生成伪布局以预训练图像编码器，在自监督框架中学习文档对象表示和定位，然后使用目标检测模型进行微调。我们展示了我们的流程在此背景下树立了新的基准，并且与现有方法和有监督对应方法相比，即使没有超越，也达到了同等性能。代码已公开于：\href{https://github.com/MaitySubhajit/SelfDocSeg}{github.com/MaitySubhajit/SelfDocSeg}。