Datasets:

rootsautomation
/

SciTSR-pd

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, 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 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

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 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:

@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}
}