| --- |
| 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} |
| } |
| ``` |
|
|
