  The lack of generalizability -- in which a model trained on one dataset
cannot provide accurate results for a different dataset -- is a known problem
in the field of document layout analysis. Thus, when a model is used to locate
important page objects in scientific literature such as figures, tables,
captions, and math formulas, the model often cannot be applied successfully to
new domains. While several solutions have been proposed, including newer and
updated deep learning models, larger hand-annotated datasets, and the
generation of large synthetic datasets, so far there is no "magic bullet" for
translating a model trained on a particular domain or historical time period to
a new field. Here we present our ongoing work in translating our document
layout analysis model from the historical astrophysical literature to the
larger corpus of scientific documents within the HathiTrust U.S. Federal
Documents collection. We use this example as an avenue to highlight some of the
problems with generalizability in the document layout analysis community and
discuss several challenges and possible solutions to address these issues. All
code for this work is available on The Reading Time Machine GitHub repository
(https://github.com/ReadingTimeMachine/htrc_short_conf).
