README.md

---
license: cc0-1.0
language:
- en
pretty_name: SciTSR-PD
size_categories:
- n<1K
task_categories:
- image-to-text
- object-detection
tags:
- table-structure-recognition
- document-understanding
- scientific-tables
- pdf
- public-domain
---

# SciTSR-PD

A public-domain subset of [SciTSR](https://github.com/Academic-Labeling/SciTSR), a large-scale table structure recognition dataset of scientific tables extracted from arXiv LaTeX source files.

This subset contains only tables whose source papers carry a **CC0 or equivalent public domain dedication** — no attribution required, no restrictions on commercial or derivative use.

## Dataset Details

| Split | Tables | Papers |
|-------|-------:|-------:|
| train | 89 | — |
| test  | 19 | — |
| **total** | **108** | **52** |

Source paper licenses present: `CC0`, `other:http://creativecommons.org/licenses/publicdomain/` (old CC public domain dedication, functionally equivalent to CC0).

## Why This Subset Exists

The full SciTSR dataset (15,000 tables) was crawled from arXiv without license filtering. ~90% of those papers use the arXiv non-exclusive license, which retains full author copyright and is not permissive for use in commercial training pipelines.

This subset was produced by querying the arXiv OAI-PMH API for the license of each source paper and retaining only those with no conditions on downstream use. See [SciTSR-CC-BY-NC-SA](https://huggingface.co/datasets/rootsautomation/SciTSR-cc-by-nc-sa) for a larger subset suitable for non-commercial open-weight model releases.

## Dataset Structure

Each row represents one table extracted from a scientific PDF.

| Column | Type | Description |
|--------|------|-------------|
| `table_id` | `string` | Unique identifier, format `{arxiv_id}v{version}.{table_index}` |
| `paper_id` | `string` | arXiv paper ID |
| `paper_title` | `string` | Paper title from arXiv metadata |
| `paper_authors` | `list[string]` | Author names from arXiv metadata |
| `paper_license` | `string` | License of the source paper |
| `split` | `string` | `train` or `test` |
| `is_comp` | `bool` | Whether this table is in the SciTSR-COMP subset (tables with at least one spanning cell) |
| `image` | `Image` | PNG render of the table (150 DPI) |
| `pdf` | `binary` | Raw PDF of the isolated table |
| `chunks` | `list[dict]` | Pre-extracted text spans with bounding box coordinates `{text, x1, x2, y1, y2}` (PDF coordinate space, bottom-left origin) |
| `cells` | `list[dict]` | Structure annotation: `{id, tex, content, start_row, end_row, start_col, end_col}` |
| `relations` | `list[dict]` | Chunk adjacency labels `{chunk_id_1, chunk_id_2, relation, num_blank}` where `relation=1` is horizontal and `relation=2` is vertical. Empty for test rows. |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("rootsautomation/SciTSR-pd")

# Iterate train split
for row in ds["train"]:
    image   = row["image"]          # PIL Image
    cells   = row["cells"]          # list of cell dicts
    chunks  = row["chunks"]         # list of chunk dicts with positions
    print(row["table_id"], row["paper_title"])
```

## Important Caveats

- **Annotation quality**: Structure annotations were generated automatically from LaTeX source by the original SciTSR pipeline. Simple grid tables are generally reliable. Tables with spanning cells (`is_comp=True`) have a higher rate of annotation errors, particularly in spanning cell coordinates. Treat annotations as noisy weak supervision rather than ground truth.
- **Chunk coordinates**: The `chunks` field was pre-processed by [TabbyCDF](https://github.com/cellsrg/tabbypdf/) and may contain noise.
- **Relations**: Available for train split only; empty list for test.

## Source & Citation

This dataset is a license-filtered derivative of SciTSR. If you use this dataset, please cite the original work:

```bibtex
@article{chi2019complicated,
  title={Complicated Table Structure Recognition},
  author={Chi, Zewen and Huang, Heyan and Xu, Heng-Da and Yu, Houjin and Yin, Wanxuan and Mao, Xian-Ling},
  journal={arXiv preprint arXiv:1908.04729},
  year={2019}
}
```