SciPostLayoutTree: A Dataset for Structural Analysis of Scientific Posters

TL;DR

This paper introduces SciPostLayoutTree, a new dataset of around 8,000 scientific posters with annotations for reading order and structure, along with a model that improves relation prediction using visual and spatial features.

Contribution

The paper presents a novel dataset for poster structural analysis and a model that effectively predicts complex spatial relations, advancing research in this area.

Findings

The dataset contains more challenging spatial relations than previous datasets.

The Layout Tree Decoder improves prediction accuracy for upward, horizontal, and long-distance relations.

Experimental results establish a new baseline for poster structure analysis.

Abstract

Scientific posters play a vital role in academic communication by presenting ideas through visual summaries. Analyzing reading order and parent-child relations of posters is essential for building structure-aware interfaces that facilitate clear and accurate understanding of research content. Despite their prevalence in academic communication, posters remain underexplored in structural analysis research, which has primarily focused on papers. To address this gap, we constructed SciPostLayoutTree, a dataset of approximately 8,000 posters annotated with reading order and parent-child relations. Compared to an existing structural analysis dataset, SciPostLayoutTree contains more instances of spatially challenging relations, including upward, horizontal, and long-distance relations. As a solution to these challenges, we develop Layout Tree Decoder, which incorporates visual features as well as bounding box features including position and category information. The model also uses beam search to predict relations while capturing sequence-level plausibility. Experimental results demonstrate that our model improves the prediction accuracy for spatially challenging relations and establishes a solid baseline for poster structure analysis. The dataset is publicly available at https://huggingface.co/datasets/omron-sinicx/scipostlayouttree. The code is also publicly available at https://github.com/omron-sinicx/scipostlayouttree.