  We formulate the task of detecting lines and paragraphs in a document into a
unified two-level clustering problem. Given a set of text detection boxes that
roughly correspond to words, a text line is a cluster of boxes and a paragraph
is a cluster of lines. These clusters form a two-level tree that represents a
major part of the layout of a document. We use a graph convolutional network to
predict the relations between text detection boxes and then build both levels
of clusters from these predictions. Experimentally, we demonstrate that the
unified approach can be highly efficient while still achieving state-of-the-art
quality for detecting paragraphs in public benchmarks and real-world images.
