  This paper describes a system prepared at Brno University of Technology for
ICDAR 2021 Competition on Historical Document Classification, experiments
leading to its design, and the main findings. The solved tasks include script
and font classification, document origin localization, and dating. We combined
patch-level and line-level approaches, where the line-level system utilizes an
existing, publicly available page layout analysis engine. In both systems,
neural networks provide local predictions which are combined into page-level
decisions, and the results of both systems are fused using linear or log-linear
interpolation. We propose loss functions suitable for weakly supervised
classification problem where multiple possible labels are provided, and we
propose loss functions suitable for interval regression in the dating task. The
line-level system significantly improves results in script and font
classification and in the dating task. The full system achieved 98.48 %, 88.84
%, and 79.69 % accuracy in the font, script, and location classification tasks
respectively. In the dating task, our system achieved a mean absolute error of
21.91 years. Our system achieved the best results in all tasks and became the
overall winner of the competition.
