Datasets:
image_id int32 1 521 | file_name stringlengths 24 135 | image imagewidth (px) 355 3.51k | width int32 355 3.51k | height int32 154 3.51k | origin_tag stringclasses 1
value | size_tag stringclasses 1
value | type_tag stringclasses 1
value | annotations dict |
|---|---|---|---|---|---|---|---|---|
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6 | 201205_B1320_a_Bewehrungsplan_Decke_ueber_Untergeschoss_BA1_3___Teil_1___untere_Lage_Liste_BF_page_2_an.png | 2,481 | 3,508 | {
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8 | 09_5903EUe093_183_1994a_Bew_GWW_Sued__Bl_7__Grundrisse___Schnitte_Biegeliste_page_12_an.png | 2,480 | 3,509 | {
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CISOL
A HuggingFace mirror of the CISOL dataset (Zenodo record 10829550, CC-BY-4.0): German construction-industry steel ordering lists annotated for table detection and table structure recognition.
Original paper: Tschirschwitz et al., WACV 2025 (arXiv:2501.15469).
License
CC-BY-4.0. The peer-reviewed paper (WACV 2025) states: "The data is licensed under the Creative Commons Attribution 4.0 International license to support full research use, subject to proper anonymization during the preparation phase." Commercial use and redistribution are permitted with attribution.
Cite the original CISOL paper (see Citation below).
Configs
| Config | Source | Images | Description |
|---|---|---|---|
td_tsr |
cisol_TD-TSR.zip |
Full document pages | End-to-end table detection + structure recognition |
tsr |
cisol_TSR.zip |
Pre-cropped table regions | Table structure recognition only |
Splits
| Split | Annotated | Available in |
|---|---|---|
train |
yes | td_tsr, tsr |
validation |
yes | td_tsr, tsr |
unlabeled |
no | td_tsr only |
No public test split: the CISOL Zenodo release includes only train and
val annotation JSON files; test annotations are withheld (standard
competition holdback). The unlabeled split (2,436 images) contains full-page
images from the same source corpus with no bounding-box annotations, useful for
self-supervised or semi-supervised pre-training.
Annotation categories
COCO-format bounding boxes covering five structural elements:
| category_name | Description |
|---|---|
table |
Full table bounding box (td_tsr only) |
row |
Individual row region |
column |
Individual column region |
spanning_cell |
Cell spanning multiple rows or columns |
header |
Header row or column region |
Schema
| Field | Type | Description |
|---|---|---|
image_id |
int32 | COCO image ID (local to each split) |
file_name |
string | Original image filename |
image |
Image | Raw image bytes (PNG/JPEG) |
width |
int32 | Image width in pixels (0 for unlabeled rows) |
height |
int32 | Image height in pixels (0 for unlabeled rows) |
origin_tag |
string | Project-origin balancing tag (empty for unlabeled) |
size_tag |
string | Document-size balancing tag (empty for unlabeled) |
type_tag |
string | Document-type balancing tag (empty for unlabeled) |
annotations |
list[struct] | COCO annotations; empty list for unlabeled rows |
Annotation struct fields: annotation_id, category_id (int32);
category_name (string); bbox (list[float32], COCO [x, y, width, height]);
area (float32); iscrowd (bool).
Segmentation masks (polygon/RLE) from the source COCO JSON are excluded; bounding-box coordinates are sufficient for TSR training.
Usage
from datasets import load_dataset
# Full-page images with detection + structure annotations
ds = load_dataset("rootsautomation/CISOL", "td_tsr")
row = ds["train"][0]
print(row["file_name"], len(row["annotations"]["bbox"]))
# Pre-cropped tables, TSR only
tsr = load_dataset("rootsautomation/CISOL", "tsr", split="train")
# Unlabeled images for pre-training
unlabeled = load_dataset("rootsautomation/CISOL", "td_tsr", split="unlabeled")
Citation
@inproceedings{tschirschwitz2025cisol,
title = {{CISOL}: An Open and Extensible Dataset for Table Structure
Recognition in the Construction Industry},
author = {Tschirschwitz, David and Gekeler, Ella and Rodehorst, Volker},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications
of Computer Vision (WACV)},
year = {2025},
}
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