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

Generalizability in Document Layout Analysis for Scientific Article Figure & Caption Extraction