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TabLeX: A Benchmark Dataset for Structure and Content Information Extraction from Scientific Tables

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Document Analysis and Recognition – ICDAR 2021 (ICDAR 2021)

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Abstract

Information Extraction (IE) from the tables present in scientific articles is challenging due to complicated tabular representations and complex embedded text. This paper presents TabLeX, a large-scale benchmark dataset comprising table images generated from scientific articles. TabLeX consists of two subsets, one for table structure extraction and the other for table content extraction. Each table image is accompanied by its corresponding LaTeX source code. To facilitate the development of robust table IE tools, TabLeX contains images in different aspect ratios and in a variety of fonts. Our analysis sheds light on the shortcomings of current state-of-the-art table extraction models and shows that they fail on even simple table images. Towards the end, we experiment with a transformer-based existing baseline to report performance scores. In contrast to the static benchmarks, we plan to augment this dataset with more complex and diverse tables at regular intervals.

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Notes

  1. 1.

    https://github.com/camelot-dev/camelot.

  2. 2.

    https://github.com/chezou/tabula-py.

  3. 3.

    https://github.com/jsvine/pdfplumber.

  4. 4.

    https://www.adobe.com/devnet/acrobat/overview.html.

  5. 5.

    http://arxiv.org/.

  6. 6.

    https://pubmed.ncbi.nlm.nih.gov/.

  7. 7.

    https://www.overleaf.com/learn/latex/font_typefaces.

  8. 8.

    https://github.com/emcconville/wand.

  9. 9.

    We use ‘400’ pixels as an experimental number.

  10. 10.

    https://github.com/jitsi/jiwer.

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Acknowledgment

This work was supported by The Science and Engineering Research Board (SERB), under sanction number ECR/2018/000087.

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Authors and Affiliations

  1. Indian Institute of Technology, Gandhinagar, India

    Harsh Desai, Pratik Kayal & Mayank Singh

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  1. Harsh Desai
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  2. Pratik Kayal
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Editor information

Editors and Affiliations

  1. Universitat Autònoma de Barcelona, Barcelona, Spain

    Josep Lladós

  2. Lehigh University, Bethlehem, PA, USA

    Daniel Lopresti

  3. Kyushu University, Fukuoka-shi, Japan

    Seiichi Uchida

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Desai, H., Kayal, P., Singh, M. (2021). TabLeX: A Benchmark Dataset for Structure and Content Information Extraction from Scientific Tables. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12822. Springer, Cham. https://doi.org/10.1007/978-3-030-86331-9_36

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  • DOI: https://doi.org/10.1007/978-3-030-86331-9_36

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  • Online ISBN: 978-3-030-86331-9

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