  Understanding documents with rich layouts is an essential step towards
information extraction. Business intelligence processes often require the
extraction of useful semantic content from documents at a large scale for
subsequent decision-making tasks. In this context, instance-level segmentation
of different document objects (title, sections, figures etc.) has emerged as an
interesting problem for the document analysis and understanding community. To
advance the research in this direction, we present a transformer-based model
called \emph{DocSegTr} for end-to-end instance segmentation of complex layouts
in document images. The method adapts a twin attention module, for semantic
reasoning, which helps to become highly computationally efficient compared with
the state-of-the-art. To the best of our knowledge, this is the first work on
transformer-based document segmentation. Extensive experimentation on
competitive benchmarks like PubLayNet, PRIMA, Historical Japanese (HJ) and
TableBank demonstrate that our model achieved comparable or better segmentation
performance than the existing state-of-the-art approaches with the average
precision of 89.4, 40.3, 83.4 and 93.3. This simple and flexible framework
could serve as a promising baseline for instance-level recognition tasks in
document images.
