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README.md

@@ -1,76 +1,92 @@
 ---
-dataset_info:
-  features:
-  - name: table_id
-    dtype: string
-  - name: paper_id
-    dtype: string
-  - name: paper_title
-    dtype: string
-  - name: paper_authors
-    list: string
-  - name: paper_license
-    dtype: string
-  - name: split
-    dtype: string
-  - name: is_comp
-    dtype: bool
-  - name: image
-    dtype: image
-  - name: pdf
-    dtype: binary
-  - name: chunks
-    list:
-    - name: text
-      dtype: string
-    - name: x1
-      dtype: float32
-    - name: x2
-      dtype: float32
-    - name: y1
-      dtype: float32
-    - name: y2
-      dtype: float32
-  - name: cells
-    list:
-    - name: id
-      dtype: int32
-    - name: tex
-      dtype: string
-    - name: content
-      list: string
-    - name: start_row
-      dtype: int32
-    - name: end_row
-      dtype: int32
-    - name: start_col
-      dtype: int32
-    - name: end_col
-      dtype: int32
-  - name: relations
-    list:
-    - name: chunk_id_1
-      dtype: int32
-    - name: chunk_id_2
-      dtype: int32
-    - name: relation
-      dtype: int32
-    - name: num_blank
-      dtype: int32
-  splits:
-  - name: train
-    num_bytes: 5224197
-    num_examples: 89
-  - name: test
-    num_bytes: 1062686
-    num_examples: 19
-  download_size: 6251183
-  dataset_size: 6286883
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+license: cc0-1.0
+language:
+- en
+pretty_name: SciTSR-PD
+size_categories:
+- n<1K
+task_categories:
+- image-to-text
+- object-detection
+tags:
+- table-structure-recognition
+- document-understanding
+- scientific-tables
+- pdf
+- public-domain
 ---
+
+# SciTSR-PD
+
+A public-domain subset of [SciTSR](https://github.com/Academic-Labeling/SciTSR), a large-scale table structure recognition dataset of scientific tables extracted from arXiv LaTeX source files.
+
+This subset contains only tables whose source papers carry a **CC0 or equivalent public domain dedication** — no attribution required, no restrictions on commercial or derivative use.
+
+## Dataset Details
+
+| Split | Tables | Papers |
+|-------|-------:|-------:|
+| train | 89 | — |
+| test  | 19 | — |
+| **total** | **108** | **52** |
+
+Source paper licenses present: `CC0`, `other:http://creativecommons.org/licenses/publicdomain/` (old CC public domain dedication, functionally equivalent to CC0).
+
+## Why This Subset Exists
+
+The full SciTSR dataset (15,000 tables) was crawled from arXiv without license filtering. ~90% of those papers use the arXiv non-exclusive license, which retains full author copyright and is not permissive for use in commercial training pipelines.
+
+This subset was produced by querying the arXiv OAI-PMH API for the license of each source paper and retaining only those with no conditions on downstream use. See [SciTSR-CC-BY-NC-SA](https://huggingface.co/datasets/rootsautomation/SciTSR-cc-by-nc-sa) for a larger subset suitable for non-commercial open-weight model releases.
+
+## Dataset Structure
+
+Each row represents one table extracted from a scientific PDF.
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `table_id` | `string` | Unique identifier, format `{arxiv_id}v{version}.{table_index}` |
+| `paper_id` | `string` | arXiv paper ID |
+| `paper_title` | `string` | Paper title from arXiv metadata |
+| `paper_authors` | `list[string]` | Author names from arXiv metadata |
+| `paper_license` | `string` | License of the source paper |
+| `split` | `string` | `train` or `test` |
+| `is_comp` | `bool` | Whether this table is in the SciTSR-COMP subset (tables with at least one spanning cell) |
+| `image` | `Image` | PNG render of the table (150 DPI) |
+| `pdf` | `binary` | Raw PDF of the isolated table |
+| `chunks` | `list[dict]` | Pre-extracted text spans with bounding box coordinates `{text, x1, x2, y1, y2}` (PDF coordinate space, bottom-left origin) |
+| `cells` | `list[dict]` | Structure annotation: `{id, tex, content, start_row, end_row, start_col, end_col}` |
+| `relations` | `list[dict]` | Chunk adjacency labels `{chunk_id_1, chunk_id_2, relation, num_blank}` where `relation=1` is horizontal and `relation=2` is vertical. Empty for test rows. |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("rootsautomation/SciTSR-pd")
+
+# Iterate train split
+for row in ds["train"]:
+    image   = row["image"]          # PIL Image
+    cells   = row["cells"]          # list of cell dicts
+    chunks  = row["chunks"]         # list of chunk dicts with positions
+    print(row["table_id"], row["paper_title"])
+```
+
+## Important Caveats
+
+- **Annotation quality**: Structure annotations were generated automatically from LaTeX source by the original SciTSR pipeline. Simple grid tables are generally reliable. Tables with spanning cells (`is_comp=True`) have a higher rate of annotation errors, particularly in spanning cell coordinates. Treat annotations as noisy weak supervision rather than ground truth.
+- **Chunk coordinates**: The `chunks` field was pre-processed by [TabbyCDF](https://github.com/cellsrg/tabbypdf/) and may contain noise.
+- **Relations**: Available for train split only; empty list for test.
+
+## Source & Citation
+
+This dataset is a license-filtered derivative of SciTSR. If you use this dataset, please cite the original work:
+
+```bibtex
+@article{chi2019complicated,
+  title={Complicated Table Structure Recognition},
+  author={Chi, Zewen and Huang, Heyan and Xu, Heng-Da and Yu, Houjin and Yin, Wanxuan and Mao, Xian-Ling},
+  journal={arXiv preprint arXiv:1908.04729},
+  year={2019}
+}
+```