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{this https URL}{github.com/MaitySubhajit/SelfDocSeg

使用自我监督技术，将文档图像中的伪造布局用于预先训练图像编码器，以在无监督的框架内学习文档对象的表示和定位，然后使用目标检测模型进行微调，该流水线在文档布局分析中表现卓越。