Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
                  first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
                  fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
                                                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
                  cls = get_filesystem_class(protocol)
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
                  raise ValueError(f"Protocol not known: {protocol}")
              ValueError: Protocol not known: memory
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 71, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Benchmarking Table Extraction from Heterogeneous Scientific PDF Documents

Published paper Extended version on arXiv Code archived on Zenodo GitHub Hugging Face Dataset Interactive demo

Dataset Sources

Dataset Details

  • PubTables (pubtables/*.tar.gz)

    • Subset of PubTables-Test dataset, enriched with HTML table ground truth
    • /images: 46,942 JPG files; one file for each page image
    • /test: 46,942 XML files containing bounding boxes in PASCAL VOC format
    • /pdfs: 23,567 PDF files from which images are taken from
    • /html: 55,991 HTML files containing extracted tables with in HTML mark-up
    • /word: 46,942 JSON files containing bounding boxes of tokens in images
  • Table-arXiv (table-arxiv/, generation recipe only, see below)

    • Dataset synthetically generated from LaTeX source files from arXiv preprints for ground truth generation.
    • The arXiv license does not let us redistribute the source preprints or anything derived from them, so the images, annotations and PDFs are not published here. We ship the list of papers we used plus the code that rebuilds the dataset from them. See Dataset Creation.
    • table-arxiv-pdf-list.csv: the 2,488 arXiv identifiers, PDF URLs and license fields the dataset was built from
    • Regenerating from that list reproduces 36,869 page images, 5,327 PASCAL VOC XML annotations, 6,441 table HTML files and 40,261 word-box JSON files
  • Table-BRGM (table-brgm.tar.gz)

    • Dataset manually annotated, domain-specific and comprises French and English geological reports sourced from a selection of BRGM
    • /images: 2003 JPG files; one file for each page image
    • /test: 251 XML files containing bounding boxes in PASCAL VOC format
    • /pdfs: 26 PDF files from which images are taken from
    • /html: 346 HTML files containing extracted tables with in HTML mark-up
    • /word: 2003 JSON files containing bounding boxes of tokens in images
    • /grid: 2003 pickle files holding the tokenizer grid input (input_ids and per-token bounding boxes) consumed by the VGT model
  • ICDAR-2013 (icdar-2013.tar.gz)

    • Dataset manually corrected from the ICDAR-2013 competition, contains European Union and US Government PDF reports.
    • /images: 238 JPG files; one file for each page image
    • /test: 135 XML files containing bounding boxes in PASCAL VOC format
    • /pdfs: 67 PDF files from which images are taken from
    • /html: 163 HTML files containing extracted tables with in HTML mark-up
    • /word: 238 JSON files containing bounding boxes of tokens in images
    • /grid: 238 pickle files holding the tokenizer grid input (input_ids and per-token bounding boxes) consumed by the VGT model

Dataset Creation

Table-arXiv is not redistributed

Table-arXiv is built from arXiv preprints, which are covered by the arXiv.org non-exclusive license to distribute. That license lets arXiv distribute the papers but does not give us the right to redistribute them, and the same restriction carries over to the page images, PDFs and table crops derived from them. We therefore publish the recipe, not the data:

  • table-arxiv/table-arxiv-pdf-list.csv: the exact 2,488 papers used, as arxiv_id, pdf_url, license. Fetch the PDFs and LaTeX sources from arXiv yourself, under arXiv's terms.
  • The generation code lives in table-arxiv-generate (scripts/table-arxiv-generate/). It compiles each paper's .tex with anchor macros injected around every \tabular environment, reads the resulting named PDF destinations to recover table bounding boxes, and emits page images, PASCAL VOC XML, per-table HTML and word-box JSON, the same layout as the other three datasets in this repo.

Running that pipeline over the published paper list reproduces the Table-arXiv split used in the paper. The other three datasets (PubTables, Table-BRGM, ICDAR-2013) are distributed here in full.

Benchmark Results

experience-data/ holds the raw model output and the evaluation scores behind the paper, for all four datasets and 15 systems (14 scored under each metric, see below).

  • <dataset>-predictions.tar.gz: one predictions_<model>.json per system, the raw extraction output before scoring. Table-BRGM and ICDAR-2013 additionally carry predictions_vgt_td.json, the table-detection-only VGT variant.
  • <dataset>-results.tar.gz: the evaluation scores, as results_<model>_final_<metric>.json where <metric> is bbox or token, plus a manifest.json recording the provenance and sha256 of every file.

The interactive demo browses a 62-page sample of these files page by page, if you would rather look than unpack.

The two variants differ in how a table counts as detected before its structure is scored. bbox (called bbox TE in the paper) requires IoU with the ground truth above 0.5. token (txt TE) replaces IoU with the Jaccard index over 2-grams of the table's text content, so that methods emitting no bounding boxes can still be evaluated.

Variant Systems evaluated
bbox cam doc gemini gpt grobid mathpix monkey pedia plum pymu qwen tatr vgt xy
token cam doc gemini got gpt grobid mathpix monkey plum pymu qwen tatr vgt xy

got (GOT-OCR 2.0) appears only under token, since it outputs no bounding boxes.

The aggregate tables are in experience-data/benchmark-results-bbox.csv and experience-data/benchmark-results-token.csv. Their headline column is TEDS, which is the end-to-end F1_TEDS score of the paper: a table extraction F1 whose per-table contribution is weighted by the TEDS structure score, so it is not a plain TEDS value. The columns map to the paper's notation as Raw = F1_bbox (resp. F1_txt) for table detection, then Topology = F1_Top, Content = F1_Con and TEDS = F1_TEDS for extraction. See Section 5.3 and Tables 4 and 8 of the extended version for the full definitions.

tatr, xy and vgt are the exception. They attach a confidence score to each detection, so instead of a single operating point they are scored with AP_TEDS, the area under the TEDS-weighted precision–recall curve swept over that threshold (paper Table 3, where they appear as TATR, XY+TATR and VGT+TATR-struct). For these three rows the same four CSV columns therefore hold AP, AP_Top, AP_Con and AP_TEDS:

Model Variant PubTables Table-arXiv Table-BRGM ICDAR-2013
tatr bbox 0.80 0.52 0.64 0.76
tatr token 0.79 0.36 0.61 0.78
xy bbox 0.78 0.51 0.73 0.71
xy token 0.77 0.37 0.68 0.73
vgt bbox 0.70 0.40 0.14 0.35
vgt token 0.64 0.14 0.04 0.15

The paper's Table 3 covers the bbox variant only. Its values match on PubTables, Table-arXiv and Table-BRGM, and differ on ICDAR-2013 (0.66, 0.63 and 0.34 in the paper), because that evaluation was rerun afterwards.

Citation

@misc{soric2025benchmarkingtableextractionheterogeneous,
      title={Benchmarking Table Extraction from Heterogeneous Scientific Extraction Documents}, 
      author={Marijan Soric and Cécile Gracianne and Ioana Manolescu and Pierre Senellart},
      year={2025},
      eprint={2511.16134},
      archivePrefix={arXiv},
      primaryClass={cs.DB},
      url={https://arxiv.org/abs/2511.16134}, 
}
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