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MediTables: A New Dataset and Deep Network for Multi-category Table Localization in Medical Documents

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Document Analysis and Recognition – ICDAR 2021 Workshops (ICDAR 2021)

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Abstract

Localizing structured layout components such as tables is an important task in document image analysis. Numerous layout datasets with document images from various domains exist. However, healthcare and medical documents represent a crucial domain that has not been included so far. To address this gap, we contribute MediTables, a new dataset of 200 diverse medical document images with multi-category table annotations. Meditables contains a wide range of medical document images with variety in capture quality, layouts, skew, occlusion and illumination. The dataset images include pathology, diagnostic and hospital-related reports. In addition to document diversity, the dataset includes implicitly structured tables that are typically not present in other datasets. We benchmark state of the art table localization approaches on the MediTables dataset and introduce a custom-designed U-Net which exhibits robust performance while being drastically smaller in size compared to strong baselines. Our annotated dataset and models represent a useful first step towards the development of focused systems for medical document image analytics, a domain that mandates robust systems for reliable information retrieval. The dataset and models can be accessed at https://github.com/atmacvit/meditables.

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Authors and Affiliations

  1. Centre for Visual Information Technology, International Institute of Information Technology, Hyderabad (IIIT-H), Hyderabad, 500032, India

    Akshay Praveen Deshpande, Vaishnav Rao Potlapalli & Ravi Kiran Sarvadevabhatla

Authors
  1. Akshay Praveen Deshpande
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  2. Vaishnav Rao Potlapalli
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  3. Ravi Kiran Sarvadevabhatla
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Corresponding author

Correspondence to Ravi Kiran Sarvadevabhatla .

Editor information

Editors and Affiliations

  1. Boise State University, Boise, ID, USA

    Elisa H. Barney Smith

  2. Indian Statistical Institute, Kolkata, India

    Umapada Pal

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Deshpande, A.P., Potlapalli, V.R., Sarvadevabhatla, R.K. (2021). MediTables: A New Dataset and Deep Network for Multi-category Table Localization in Medical Documents. In: Barney Smith, E.H., Pal, U. (eds) Document Analysis and Recognition – ICDAR 2021 Workshops. ICDAR 2021. Lecture Notes in Computer Science(), vol 12916. Springer, Cham. https://doi.org/10.1007/978-3-030-86198-8_9

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  • DOI: https://doi.org/10.1007/978-3-030-86198-8_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86197-1

  • Online ISBN: 978-3-030-86198-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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