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 Sources

• Repository: https://huggingface.co/datasets/rootsautomation/pubmed-ocr
• Paper: PubMed-OCR: PMC Open Access OCR Annotations
• 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):

{
  "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:

@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

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:

ds = load_dataset("rootsautomation/pubmed-ocr", split="train", streaming=True)
for row in ds:
    ocr = json.loads(row["ocr_json"])
    ...