TexBiG (from the German Text-Bild-Gefüge, meaning Text-Image-Structure) is a document layout analysis dataset for historical documents in the late 19th and early 20th century.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.

The added test images can be used to make submission on the leaderboard on EvalAI: https://eval.ai/web/challenges/challenge-page/2078/overview

Dataset link: https://doi.org/10.5281/zenodo.8347059

Dataset (only train): https://www.kaggle.com/datasets/davidtschirschwitz/texbig-v2-0-train-val

Dataset (only test image): https://www.kaggle.com/datasets/davidtschirschwitz/texbig-v2-0-test

Please use TexBiG 2023 (which is v2.0 of the dataset) for testing model performance. The test dataset from the 2022 version (v1.0) are included as training data in v2.0

Each image of the dataset was annotated at least by two different annotators.

TexBiG (from the German Text-Bild-Gefüge, meaning Text-Image-Structure) is a document layout analysis dataset for historical documents in the late 19th and early 20th century.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.