  Abstract--- Table detection and extraction has been studied in the context of
documents like reports, where tables are clearly outlined and stand out from
the document structure visually. We study this topic in a rather more
challenging domain of layout-heavy business documents, particularly invoices.
Invoices present the novel challenges of tables being often without outlines -
either in the form of borders or surrounding text flow - with ragged columns
and widely varying data content. We will also show, that we can extract
specific information from structurally different tables or table-like
structures with one model. We present a comprehensive representation of a page
using graph over word boxes, positional embeddings, trainable textual features
and rephrase the table detection as a text box labeling problem. We will work
on our newly presented dataset of pro forma invoices, invoices and debit note
documents using this representation and propose multiple baselines to solve
this labeling problem. We then propose a novel neural network model that
achieves strong, practical results on the presented dataset and analyze the
model performance and effects of graph convolutions and self-attention in
detail.
