1 dataset found
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    TexBiG

    • webis.de
    • anthology.aicmu.ac.cn
    6885143
    Updated 2022
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    Volker Rodehorst; Benno Stein (2022). TexBiG [Dataset]. http://doi.org/10.5281/zenodo.6885143
    Explore at:
    6885143Available download formats
    Dataset updated
    2022
    Dataset provided by
    Bauhaus-Universität Weimar
    The Web Technology & Information Systems Network
    Authors
    Volker Rodehorst; Benno Stein
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    TexBiG (from the German Text-Bild-Gefüge, meaning Text-Image-Structure) is a document layout analysis dataset for historical documents in the late 19th and early 20th century. The dataset provides instance segmentation (bounding boxes and polygons/masks) annotations for 19 different classes with more then 52.000 instances. Annotations are manually annotated by experts and evaluated with Krippendorff's Alpha, for each document image are least two different annotators have labeled the document. The dataset uses the common COCO-JSON format.

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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Volker Rodehorst; Benno Stein (2022). TexBiG [Dataset]. http://doi.org/10.5281/zenodo.6885143

TexBiG

Explore at:
20 scholarly articles cite this dataset (View in Google Scholar)
6885143Available download formats
Dataset updated
2022
Dataset provided by
Bauhaus-Universität Weimar
The Web Technology & Information Systems Network
Authors
Volker Rodehorst; Benno Stein
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

TexBiG (from the German Text-Bild-Gefüge, meaning Text-Image-Structure) is a document layout analysis dataset for historical documents in the late 19th and early 20th century. The dataset provides instance segmentation (bounding boxes and polygons/masks) annotations for 19 different classes with more then 52.000 instances. Annotations are manually annotated by experts and evaluated with Krippendorff's Alpha, for each document image are least two different annotators have labeled the document. The dataset uses the common COCO-JSON format.

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