| ---
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| license: apache-2.0
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| pretty_name: DocBank (sampled) — document pages with token-level OCR ground truth
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| task_categories:
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| - image-to-text
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| - object-detection
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| language:
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| - en
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| size_categories:
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| - 1K<n<10K
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| tags:
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| - docbank
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| - ocr
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| - document-ai
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| - document-layout
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| - layout-analysis
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| - text-recognition
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| - arxiv
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| source_datasets:
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| - doc-analysis/DocBank
|
| ---
|
|
|
| # DocBank (sampled) — document pages + exact OCR ground truth
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|
|
| 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.
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|
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| 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.
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|
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| 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?
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|
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| Three frictions make raw DocBank awkward to ingest:
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|
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| 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.
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| 2. **Loose per-page text files.** Annotations are one `.txt` per page (tab-separated tokens) inside a 3 GB zip — millions of tiny files.
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| 3. **Scale.** 500K pages is far more than a test corpus needs.
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|
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| 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.
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|
|
| ## What this repo contains
|
|
|
| ```
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| docbank-train-images.zip # 5,000 page images (PNG/JPG), zip entry name == page_id + ext
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| docbank-train.jsonl.gz # 5,000 lines, one JSON object per page
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| docbank-test-images.zip # 1,000 page images
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| docbank-test.jsonl.gz # 1,000 lines
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| ```
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|
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| ### Annotation JSONL — one page per line
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|
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| ```json
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| {
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| "page_id": "2004.xxxxx.tar_2004.01234.gz_paper_3_ori",
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| "image_file": "2004.xxxxx.tar_2004.01234.gz_paper_3_ori.jpg",
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| "width": 612,
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| "height": 792,
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| "text": "Full-page reading-order text, tokens space-joined ...",
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| "tokens": [
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| {"text": "Attention", "x0": 120, "y0": 88, "x1": 210, "y1": 104, "label": "title", "font": "NimbusRomNo9L-Medi"}
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| ]
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| }
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| ```
|
|
|
| - `image_file` is the **exact** entry name inside the images zip — join on it directly, no filename parsing.
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| - `text` is every token joined by spaces in reading order — ready for an OCR-vs-truth diff.
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| - `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`.
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|
|
| ### The twelve semantic labels
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|
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| `title`, `author`, `abstract`, `paragraph`, `section`, `list`, `caption`, `equation`, `figure`, `table`, `reference`, `footer`.
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|
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| ## ⚠️ Ground truth is machine-perfect, not human
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|
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| 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.
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|
|
| ## How to use
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|
|
| ```python
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| import gzip, json, zipfile, io
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| from PIL import Image
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|
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| # annotations
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| with gzip.open("docbank-test.jsonl.gz", "rt", encoding="utf-8") as f:
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| pages = [json.loads(line) for line in f]
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| print(len(pages), pages[0]["text"][:200])
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|
|
| # images — join by image_file
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| with zipfile.ZipFile("docbank-test-images.zip") as z:
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| img = Image.open(io.BytesIO(z.read(pages[0]["image_file"])))
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| print(img.size, "vs", (pages[0]["width"], pages[0]["height"]))
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| ```
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|
|
| ## Dataset specs
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|
|
| | | Spec |
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| |---|---|
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| | Pages | 5,000 train / 1,000 test (sampled from DocBank's official splits) |
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| | Row granularity | one page (image + full text + token array) |
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| | Bounding boxes | per token, normalized to 0–1000 |
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| | Semantic labels | 12 (title / author / abstract / paragraph / section / list / caption / equation / figure / table / reference / footer) |
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| | Domain | academic papers (arXiv), English, two-column + equations |
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| | Format | images as zip per split; annotations as gzipped JSONL |
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| | Ground truth | machine-exact (from LaTeX source) — use fuzzy scoring |
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|
|
| ## When to pick DocBank
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|
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| - **Document OCR eval**: run a recognizer over the page image, diff against the exact `text`. Real printed pages, not synthetic, not scene text.
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| - **Layout / detection**: token boxes with semantic labels support detection and reading-order experiments.
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| - **Document-AI prototyping**: per-token text + box + role is the shape LayoutLM-style models consume.
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|
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| For photographed text in the wild use **TextOCR** / **HierText**; for receipts, **CORD**; for isolated handwritten characters, **EMNIST**.
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|
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| ## License
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|
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| **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.
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|
|
| - Paper: [DocBank: A Benchmark Dataset for Document Layout Analysis](https://arxiv.org/abs/2006.01038)
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| - Upstream: [doc-analysis/DocBank](https://github.com/doc-analysis/DocBank) · [liminghao1630/DocBank](https://huggingface.co/datasets/liminghao1630/DocBank)
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|
