| --- |
| language: |
| - en |
| license: other |
| size_categories: |
| - 1M<n<10M |
| task_categories: |
| - image-to-text |
| - image-text-to-text |
| pretty_name: PubMed-OCR |
| arxiv: 2601.11425 |
| dataset_info: |
| features: |
| - name: basename |
| dtype: string |
| - name: page |
| dtype: int32 |
| - name: license |
| dtype: string |
| - name: pmid |
| dtype: string |
| - name: accession_id |
| dtype: string |
| - name: article_citation |
| dtype: string |
| - name: pdf_bytes |
| dtype: binary |
| - name: ocr_json |
| dtype: string |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: train-*.parquet |
| license_name: pubmed-ocr-multiple-cc-licenses |
| tags: |
| - biology |
| - medical |
| - ocr |
| - multimodal |
| --- |
| |
| # PubMed-OCR: PMC Open Access OCR Annotations |
|
|
| PubMed-OCR is an OCR-centric corpus of scientific articles derived from PubMed Central Open Access PDFs. Each **page** is rendered to an image and annotated with **Google Cloud Vision OCR**, released in a compact JSON schema with **word-, line-, and paragraph-level** bounding boxes. |
|
|
| **Scale (release):** |
| - **209.5K** articles |
| - **~1.5M** pages |
| - **~1.3B** words (OCR tokens) |
|
|
| This dataset is intended to support layout-aware modeling, coordinate-grounded QA, and evaluation of OCR-dependent pipelines on scientific documents. |
|
|
| ## Dataset Details |
|
|
| ### Dataset Description |
|
|
| - **Curated by:** Roots.ai |
| - **Point of contact:** ai-ml@roots.ai |
| - **Language:** English (primarily; see limitations) |
| - **Data unit:** **1 row = 1 PDF page** (unique by `{basename, page}`) |
| - **License:** See **Licensing** section (source-article licenses; per-row `license` field) |
|
|
| ### Dataset Sources |
|
|
| - **Repository:** https://huggingface.co/datasets/rootsautomation/pubmed-ocr |
| - **Paper:** [PubMed-OCR: PMC Open Access OCR Annotations](https://huggingface.co/papers/2601.11425) |
| - **Source corpus:** PubMed Central Open Access (PMCOA) |
|
|
| ## Uses |
|
|
| ### Direct Use |
|
|
| PubMed-OCR is suitable for: |
| - Training/evaluating **OCR-aware** or **layout-aware** document models |
| - Testing robustness of pipelines that depend on OCR (parsing, retrieval, extraction) |
| - Building tasks that require **coordinate-grounded evidence** (e.g., quote-and-locate, region attribution) |
| - Benchmark curation for scientific PDFs (tables, formulas, captions, references) |
|
|
| ### Out-of-Scope Use |
|
|
| - Do **not** treat OCR output as gold text; it contains recognition errors. |
| - Not intended for clinical/medical decision-making. |
| - Not intended for learning copyrighted content outside the applicable license terms. |
| - Not intended as a reading-order ground truth dataset. |
|
|
| ## Dataset Structure |
|
|
| ### Data Instances |
|
|
| Each row corresponds to a single page. Key identifiers: |
| - `basename`: page group identifier (article-level) |
| - `page`: page index within the article |
|
|
| `ocr_json` is a JSON string containing OCR outputs with bounding boxes in **pixel coordinates** for the rendered page image. |
|
|
| Example (schema sketch; fields may include additional metadata): |
|
|
| ```json |
| { |
| "image": {"width": 1275, "height": 1650, "dpi": 150}, |
| "text": { |
| "words": [{"text": "Introduction", "bbox": [74, 132, 210, 156]}], |
| "lines": [{"text": "Introduction", "bbox": [74, 130, 612, 160]}], |
| "paragraphs": [{"text": "…", "bbox": [70, 120, 1180, 420]}] |
| } |
| } |
| ``` |
|
|
| ### Data Fields |
|
|
| * `basename` *(string)*: article/page group identifier. |
| * `page` *(int32)*: page index within the PDF/article. |
| * `license` *(string)*: the **source article’s license** (e.g., `cc-by-4.0`, `cc-by-nc-4.0`, …). |
| * `pmid` *(string)*: PubMed ID when available. |
| * `accession_id` *(string)*: accession identifier (e.g., PMCID or internal ID). |
| * `article_citation` *(string)*: a citation string for the source article. |
| * `pdf_bytes` *(binary)*: raw PDF bytes **when redistribution is permitted**; may be empty/null otherwise. |
| * `ocr_json` *(string)*: OCR output JSON (see above). |
|
|
| ### Splits |
|
|
| This release is provided as a single split (`train`) because it is primarily a **corpus**. |
| For benchmarking, consider constructing evaluation splits that reduce leakage, e.g.: |
|
|
| * **Journal-level splits** (hold out entire journals) |
| * **Time-based splits** (hold out by publication year) |
| * **PMID/PMCID disjoint splits** (article-level separation) |
|
|
| ## Dataset Creation |
|
|
| ### Curation Rationale |
|
|
| Scientific PDFs are dense (formulas, tables, multi-column layouts). Many PMCOA datasets rely on PDF/XML alignment, which can miss scanned pages or inherit parser noise. PubMed-OCR provides OCR-native supervision directly from rendered page images, enabling OCR-dependent evaluation and layout-aware learning without PDF/XML alignment. |
|
|
| ### Source Data |
|
|
| #### Data Collection and Processing |
|
|
| High-level pipeline: |
|
|
| 1. Download PubMed Central Open Access PDFs (PMCOA) and filter to licenses permitting redistribution of derived artifacts. |
| 2. Uniformly sample 209.5K documents. |
| 3. Render each page at **150 DPI**. |
| 4. Run **Google Cloud Vision** `document_text_detection` on page images. |
| 5. Extract word- and paragraph-level polygons and canonicalize to axis-aligned bboxes `[x1, y1, x3, y3]`. |
| 6. Reconstruct **line** bboxes by clustering words with similar vertical alignment (heuristic). |
| 7. Emit one row per page with `ocr_json` (+ `pdf_bytes` where permitted). |
|
|
| #### Who are the source data producers? |
|
|
| The source texts were authored by scientific article authors and published via journals hosted in PubMed Central Open Access. |
|
|
| ### Annotations |
|
|
| #### Annotation process |
|
|
| Annotations are machine-generated via Google Cloud Vision OCR. |
|
|
| * **Words / paragraphs:** provided by the OCR engine |
| * **Lines:** reconstructed heuristically from word boxes (see Limitations) |
|
|
| #### Who are the annotators? |
|
|
| The OCR engine is the annotator. No manual annotation was performed in this release. |
|
|
| #### Personal and Sensitive Information |
|
|
| Scientific articles can contain author names, affiliations, acknowledgements, emails, and citations. Content is drawn from publicly available PMCOA articles; no additional anonymization is applied. |
|
|
| ## Bias, Risks, and Limitations |
|
|
| * **Single OCR engine:** outputs reflect Google Vision’s strengths/weaknesses and may not generalize to other OCR systems. |
| * **Heuristic line reconstruction:** line grouping and reading order can be imperfect, especially in multi-column layouts and around formulas/tables. |
| * **Axis-aligned boxes:** original OCR polygons are simplified to rectangles. |
| * **Domain skew:** PMCOA’s journal distribution is heavy-tailed (high-volume journals dominate). |
| * **Non-text regions:** this dataset does not provide gold structure for tables/figures/formulas (only what OCR emits + derived lines). |
|
|
| ### Recommendations |
|
|
| * When reporting results, specify whether you use **words**, **lines**, or **paragraphs**, and whether you re-linearize text. |
| * For fair evaluation, prefer **journal-disjoint** or **article-disjoint** splits. |
| * If you need table/figure structure, pair this with a layout/table dataset (or run a layout model on top). |
|
|
| ## Licensing |
|
|
| This dataset contains content derived from PMCOA articles. |
|
|
| * Each example inherits the **license of its source article**, recorded in the `license` field. |
| * Users are responsible for complying with the license terms for any subset they use. |
| * If `pdf_bytes` is present, it is provided only where redistribution is permitted. |
|
|
| ## Citation |
|
|
| If you use PubMed-OCR, please cite: |
|
|
| ```bibtex |
| @article{heidenreich2025pubmedocr, |
| title={PubMed-OCR: PMC Open Access OCR Annotations}, |
| author={Heidenreich, Hunter and Getachew, Yosheb and Dinica, Olivia and Elliott, Ben}, |
| journal={arXiv preprint arXiv:2601.11425}, |
| year={2025} |
| } |
| ``` |
|
|
| ## How to Load |
|
|
| ```python |
| from datasets import load_dataset |
| import json |
| |
| ds = load_dataset("rootsautomation/pubmed-ocr", split="train") |
| |
| row = ds[0] |
| ocr = json.loads(row["ocr_json"]) |
| words = ocr["text"]["words"] |
| ``` |
|
|
| For large-scale iteration, consider streaming: |
|
|
| ```python |
| ds = load_dataset("rootsautomation/pubmed-ocr", split="train", streaming=True) |
| for row in ds: |
| ocr = json.loads(row["ocr_json"]) |
| ... |
| ``` |
|
|
| ## Models Trained on this Data |
|
|
| - [GutenOCR-3B](https://huggingface.co/rootsautomation/GutenOCR-3B) |
| - [GutenOCR-7B](https://huggingface.co/rootsautomation/GutenOCR-7B) |
