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README.md

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+---
+license: apache-2.0
+pretty_name: DocBank (sampled) — document pages with token-level OCR ground truth
+task_categories:
+  - image-to-text
+  - object-detection
+language:
+  - en
+size_categories:
+  - 1K<n<10K
+tags:
+  - docbank
+  - ocr
+  - document-ai
+  - document-layout
+  - layout-analysis
+  - text-recognition
+  - arxiv
+source_datasets:
+  - doc-analysis/DocBank
+---
+
+# DocBank (sampled) — document pages + exact OCR ground truth
+
+A sampled, reshaped mirror of [DocBank](https://github.com/doc-analysis/DocBank) (Li et al., COLING 2020), packaged for one-row-per-page ingestion. The upstream release is 54 GB with page images split across a **ten-part** archive (`DocBank_500K_ori_img.zip.001`…`.010`) and annotations as a separate 3 GB zip of per-page `.txt` files. This mirror carries a small cut — **5,000 train + 1,000 test pages** — with images repackaged into a single zip per split and the token annotations reshaped into gzipped JSONL, so a pipeline can fetch one split and join image ↔ annotation without reassembling a multi-part archive or parsing thousands of loose text files.
+
+Re-hosted under Heliosoph for ingestion-pipeline stability; the page pixels and token ground truth are DocBank's, unchanged in content. Pin a revision for reproducible fetches.
+
+Credit: Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, Ming Zhou (DocBank). Source documents are LaTeX submissions on arXiv.org.
+
+## Why a mirror?
+
+Three frictions make raw DocBank awkward to ingest:
+
+1. **Ten-part split zip.** The images ship as `*.zip.001` … `*.zip.010`; you must download all ~54 GB and reassemble before a single page is readable.
+2. **Loose per-page text files.** Annotations are one `.txt` per page (tab-separated tokens) inside a 3 GB zip — millions of tiny files.
+3. **Scale.** 500K pages is far more than a test corpus needs.
+
+This mirror resolves all three: a bounded sample, images as one zip per split, and annotations pre-joined into JSONL (one page per line). The token text and boxes are byte-faithful to DocBank's `.txt` ground truth.
+
+## What this repo contains
+
+```
+docbank-train-images.zip   # 5,000 page images (PNG/JPG), zip entry name == page_id + ext
+docbank-train.jsonl.gz     # 5,000 lines, one JSON object per page
+docbank-test-images.zip    # 1,000 page images
+docbank-test.jsonl.gz      # 1,000 lines
+```
+
+### Annotation JSONL — one page per line
+
+```json
+{
+  "page_id": "2004.xxxxx.tar_2004.01234.gz_paper_3_ori",
+  "image_file": "2004.xxxxx.tar_2004.01234.gz_paper_3_ori.jpg",
+  "width": 612,
+  "height": 792,
+  "text": "Full-page reading-order text, tokens space-joined ...",
+  "tokens": [
+    {"text": "Attention", "x0": 120, "y0": 88, "x1": 210, "y1": 104, "label": "title", "font": "NimbusRomNo9L-Medi"}
+  ]
+}
+```
+
+- `image_file` is the **exact** entry name inside the images zip — join on it directly, no filename parsing.
+- `text` is every token joined by spaces in reading order — ready for an OCR-vs-truth diff.
+- `tokens[]` preserves DocBank's per-token ground truth: `text`, the bounding box `(x0,y0)`–`(x1,y1)` **normalized to 0–1000**, one of twelve semantic `label`s, and the `font`.
+
+### The twelve semantic labels
+
+`title`, `author`, `abstract`, `paragraph`, `section`, `list`, `caption`, `equation`, `figure`, `table`, `reference`, `footer`.
+
+## ⚠️ Ground truth is machine-perfect, not human
+
+DocBank's labels come from the LaTeX **source**, not from reading the pixels. The transcription is exact by construction — but it reflects source tokenization (ligatures, hyphenation, math markup) rather than what a pixel-level OCR model would emit. Evaluate with a fuzzy / normalized-edit-distance metric, not exact string match. Bounding boxes are in a 0–1000 normalized space; multiply by `width/1000` and `height/1000` to project onto the image.
+
+## How to use
+
+```python
+import gzip, json, zipfile, io
+from PIL import Image
+
+# annotations
+with gzip.open("docbank-test.jsonl.gz", "rt", encoding="utf-8") as f:
+    pages = [json.loads(line) for line in f]
+print(len(pages), pages[0]["text"][:200])
+
+# images — join by image_file
+with zipfile.ZipFile("docbank-test-images.zip") as z:
+    img = Image.open(io.BytesIO(z.read(pages[0]["image_file"])))
+    print(img.size, "vs", (pages[0]["width"], pages[0]["height"]))
+```
+
+## Dataset specs
+
+| | Spec |
+|---|---|
+| Pages | 5,000 train / 1,000 test (sampled from DocBank's official splits) |
+| Row granularity | one page (image + full text + token array) |
+| Bounding boxes | per token, normalized to 0–1000 |
+| Semantic labels | 12 (title / author / abstract / paragraph / section / list / caption / equation / figure / table / reference / footer) |
+| Domain | academic papers (arXiv), English, two-column + equations |
+| Format | images as zip per split; annotations as gzipped JSONL |
+| Ground truth | machine-exact (from LaTeX source) — use fuzzy scoring |
+
+## When to pick DocBank
+
+- **Document OCR eval**: run a recognizer over the page image, diff against the exact `text`. Real printed pages, not synthetic, not scene text.
+- **Layout / detection**: token boxes with semantic labels support detection and reading-order experiments.
+- **Document-AI prototyping**: per-token text + box + role is the shape LayoutLM-style models consume.
+
+For photographed text in the wild use **TextOCR** / **HierText**; for receipts, **CORD**; for isolated handwritten characters, **EMNIST**.
+
+## License
+
+**Apache-2.0**, as released by the DocBank authors. Permits commercial use, modification, and redistribution with attribution. The underlying documents are arXiv submissions used under their distribution terms; cite the DocBank paper and arXiv.
+
+- Paper: [DocBank: A Benchmark Dataset for Document Layout Analysis](https://arxiv.org/abs/2006.01038)
+- Upstream: [doc-analysis/DocBank](https://github.com/doc-analysis/DocBank) · [liminghao1630/DocBank](https://huggingface.co/datasets/liminghao1630/DocBank)

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