  Document Layout Analysis, which is the task of identifying different semantic
regions inside of a document page, is a subject of great interest for both
computer scientists and humanities scholars as it represents a fundamental step
towards further analysis tasks for the former and a powerful tool to improve
and facilitate the study of the documents for the latter. However, many of the
works currently present in the literature, especially when it comes to the
available datasets, fail to meet the needs of both worlds and, in particular,
tend to lean towards the needs and common practices of the computer science
side, leading to resources that are not representative of the humanities real
needs. For this reason, the present paper introduces U-DIADS-Bib, a novel,
pixel-precise, non-overlapping and noiseless document layout analysis dataset
developed in close collaboration between specialists in the fields of computer
vision and humanities. Furthermore, we propose a novel, computer-aided,
segmentation pipeline in order to alleviate the burden represented by the
time-consuming process of manual annotation, necessary for the generation of
the ground truth segmentation maps. Finally, we present a standardized few-shot
version of the dataset (U-DIADS-BibFS), with the aim of encouraging the
development of models and solutions able to address this task with as few
samples as possible, which would allow for more effective use in a real-world
scenario, where collecting a large number of segmentations is not always
feasible.
