On scientific literature parsing

Scientific literature contain important information related to cutting-edge innovations in diverse domains. Advances in natural language processing have been driving the fast development in automated information extraction from scientific literature. But scientific literature are mainly in unstructured PDF format. But while PDF is great for preserving the basic elements (characters, lines, shapes, images, etc.) on a canvas for different operating systems or devices for humans to consume, it’s not a format that machines can understand.

A critical challenge for automated information extraction from scientific literature is that the documents often contain content that is not in natural language, such as figures and table. Nevertheless, such content usually illustrates key results, messages, or summarizations of the research. To obtain a comprehensive understanding of scientific literature, the automated system must be able to recognize the layout of the documents and parse the non-natural-language content into a machine readable format. This competition aims to drive the advances in the following two problems:

  • Document layout recognition
  • Table recognition

Task A: Document layout recognition


Task B: Table recognition


  • Dataset: PubTabNet
  • Task: Converting table images into HTML code
  • Metric: TEDS
  • Participate: https://aieval.draco.res.ibm.com/challenge/40/overview
  • Important dates:
    • 20th July, 2020: Open for test submission
    • 28th March, 2021: Open for final evaluation submission
    • 31st March, 2021: Submission close
    • 1st May, 2021: Announcement of winning team

Terms and conditions


  • One team must be registered with one and only one account.
  • The maximum team size is 8 people.
  • Team mergers are NOT allowed.
  • This competition is open to every one, except to those who have a conflict of interest with the organizers.
  • Winning teams are strongly encouraged to release their code to help the community to reproduce their results and develop better models. If code cannot be released, winning teams must at least allow the organizers to reproduce their method and results for legitimacy verification.
  • For entries made by non-IBM employees IBM will make no claim to ownership of your entry or any intellectual property that it may contain. When submitting model source code, the code will be used for the sole purpose of verifying the legitimacy of the submission. Participants keep copyright to their code submissions.
  • It is allowed to use additional third-party data or pre-trained models for performance improvement.
  • Participants MUST NOT include human intervention in their method or take any manner to reverse engineer the ground truth of the test set, which will be treated as cheating and the participants will be banned to take part in this competition and future continuous competitions.

For any question, please contact: steve6211942@gmail.com

Copyright © ICDAR 2021 Organizing Committee