Tiramisu demonstrates that explicitly modeling document hierarchy through tiered transformer attention can outperform traditional bottom-up pipelines for understanding complex newspaper layouts, offering a scalable alternative for historical document digitization.
This paper tackles understanding complex newspaper layouts by proposing two approaches: a modular pipeline combining existing models (YOLO, LayoutReader) for layout detection and reading order, and Tiramisu, a novel transformer architecture that hierarchically processes newspaper structure end-to-end.