  Handwritten Text Recognition (HTR) is an open problem at the intersection of
Computer Vision and Natural Language Processing. The main challenges, when
dealing with historical manuscripts, are due to the preservation of the paper
support, the variability of the handwriting -- even of the same author over a
wide time-span -- and the scarcity of data from ancient, poorly represented
languages. With the aim of fostering the research on this topic, in this paper
we present the Ludovico Antonio Muratori (LAM) dataset, a large line-level HTR
dataset of Italian ancient manuscripts edited by a single author over 60 years.
The dataset comes in two configurations: a basic splitting and a date-based
splitting which takes into account the age of the author. The first setting is
intended to study HTR on ancient documents in Italian, while the second focuses
on the ability of HTR systems to recognize text written by the same writer in
time periods for which training data are not available. For both
configurations, we analyze quantitative and qualitative characteristics, also
with respect to other line-level HTR benchmarks, and present the recognition
performance of state-of-the-art HTR architectures. The dataset is available for
download at \url{https://aimagelab.ing.unimore.it/go/lam}.
