| --- |
| 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}, |
| } |
| ``` |
|
|
