  Layout Analysis (the identification of zones and their classification) is the
first step along line segmentation in Optical Character Recognition and similar
tasks. The ability of identifying main body of text from marginal text or
running titles makes the difference between extracting the work full text of a
digitized book and noisy outputs. We show that most segmenters focus on pixel
classification and that polygonization of this output has not been used as a
target for the latest competition on historical document (ICDAR 2017 and
onwards), despite being the focus in the early 2010s. We propose to shift, for
efficiency, the task from a pixel classification-based polygonization to an
object detection using isothetic rectangles. We compare the output of Kraken
and YOLOv5 in terms of segmentation and show that the later severely
outperforms the first on small datasets (1110 samples and below). We release
two datasets for training and evaluation on historical documents as well as a
new package, YALTAi, which injects YOLOv5 in the segmentation pipeline of
Kraken 4.1.
