README.md

---
language:
- en
license: cc
size_categories:
- 1K<n<10K
task_categories:
- OTHER
tags:
- scientific-posters
- layout-analysis
- document-structure-analysis
---

# SciPostLayoutTree: A Dataset for Structural Analysis of Scientific Posters

This repository contains the SciPostLayoutTree dataset, introduced in the paper "[SciPostLayoutTree: A Dataset for Structural Analysis of Scientific Posters](https://huggingface.co/papers/2511.18329)".

SciPostLayoutTree is a dataset of approximately 8,000 scientific posters annotated with reading order and parent-child relations. It is designed to facilitate research into structural analysis of scientific posters, which play a vital role in academic communication. The dataset specifically addresses challenges related to spatially complex relations, including upward, horizontal, and long-distance relationships, making it a valuable resource for building structure-aware interfaces.

- **Paper**: [SciPostLayoutTree: A Dataset for Structural Analysis of Scientific Posters](https://huggingface.co/papers/2511.18329)
- **Code/GitHub Repository**: [https://github.com/omron-sinicx/scipostlayouttree](https://github.com/omron-sinicx/scipostlayouttree)

For detailed dataset construction, experimental setup, and reproduction steps, please refer to the comprehensive instructions provided in the [GitHub repository](https://github.com/omron-sinicx/scipostlayouttree).

### Sample Usage

You can visualize tree annotations using the script provided in the associated GitHub repository. First, install the necessary Python packages:

```bash
pip install opencv-python numpy matplotlib tqdm
```

Then, execute the visualization script:

```bash
python visualize_annotation.py
```

Please ensure you have completed the [Dataset Construction steps](https://github.com/omron-sinicx/scipostlayouttree#dataset-construction) outlined in the GitHub repository to prepare the data and annotation files before running the visualization.