| 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. |