TexBiG (from the German Text-Bild-Gefüge, meaning Text-Image-Structure) is a document layout analysis dataset for historical documents in the late 19th and early 20th century.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

This is the dataset for the paper "A Dataset for Analysing Complex Document Layouts in the Digital Humanities and its Evaluation with Krippendorff ’s Alpha" in its second version, containing an update of the test images (without annotations) from the paper "Drawing the Same Bounding Box Twice? Coping Noisy Annotations in Object Detection with Repeated Labels". Organization of the dataset is also updated to make it easier to use.

TexBiG (from the German Text-Bild-Gefüge, meaning Text-Image-Structure) is a document layout analysis dataset for historical documents in the late 19th and early 20th century.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

The annotation guideline can be found in the first of the dataset.