Datasets:
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license: cc-by-4.0
language:
- de
task_categories:
- object-detection
pretty_name: CISOL
tags:
- table-detection
- table-structure-recognition
- document-understanding
- construction-industry
---
# CISOL
A HuggingFace mirror of the [CISOL](https://zenodo.org/records/10829550) 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
```python
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
```bibtex
@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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