  Archivists, textual scholars, and historians often produce digital editions
of historical documents. Using markup schemes such as those of the Text
Encoding Initiative and EpiDoc, these digital editions often record documents'
semantic regions (such as notes and figures) and physical features (such as
page and line breaks) as well as transcribing their textual content. We
describe methods for exploiting this semantic markup as distant supervision for
training and evaluating layout analysis models. In experiments with several
model architectures on the half-million pages of the Deutsches Textarchiv
(DTA), we find a high correlation of these region-level evaluation methods with
pixel-level and word-level metrics. We discuss the possibilities for improving
accuracy with self-training and the ability of models trained on the DTA to
generalize to other historical printed books.
