  Transforming documents into machine-processable representations is a
challenging task due to their complex structures and variability in formats.
Recovering the layout structure and content from PDF files or scanned material
has remained a key problem for decades. ICDAR has a long tradition in hosting
competitions to benchmark the state-of-the-art and encourage the development of
novel solutions to document layout understanding. In this report, we present
the results of our \textit{ICDAR 2023 Competition on Robust Layout Segmentation
in Corporate Documents}, which posed the challenge to accurately segment the
page layout in a broad range of document styles and domains, including
corporate reports, technical literature and patents. To raise the bar over
previous competitions, we engineered a hard competition dataset and proposed
the recent DocLayNet dataset for training. We recorded 45 team registrations
and received official submissions from 21 teams. In the presented solutions, we
recognize interesting combinations of recent computer vision models, data
augmentation strategies and ensemble methods to achieve remarkable accuracy in
the task we posed. A clear trend towards adoption of vision-transformer based
methods is evident. The results demonstrate substantial progress towards
achieving robust and highly generalizing methods for document layout
understanding.
