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Page layout analysis is a fundamental step in document processing whichenables to segment a page into regions of interest. With highly complex layoutsand mixed scripts, scholarly commentaries are text-heavy documents which remainchallenging for state-of-the-art models. Their layout considerably variesacross editions and their most important regions are mainly defined by semanticrather than graphical characteristics such as position or appearance. Thissetting calls for a comparison between textual, visual and hybrid approaches.We therefore assess the performances of two transformers (LayoutLMv3 andRoBERTa) and an objection-detection network (YOLOv5). If results show a clearadvantage in favor of the latter, we also list several caveats to this finding.In addition to our experiments, we release a dataset of ca. 300 annotated pagessampled from 19th century commentaries.

Page Layout Analysis of Text-heavy Historical Documents: a Comparison of Textual and Visual Approaches