  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, 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: https://github.com/MaitySubhajit/SelfDocSeg
