| --- |
| pretty_name: SafeOCR-LabGold |
| license: apache-2.0 |
| language: |
| - en |
| size_categories: |
| - n<1K |
| tags: |
| - clinical-ocr |
| - healthcare |
| - synthetic |
| - verification |
| - fhir |
| - benchmark |
| configs: |
| - config_name: default |
| default: true |
| data_files: |
| - split: calibration |
| path: "viewer/calibration.parquet" |
| - split: evaluation |
| path: "viewer/evaluation.parquet" |
| --- |
| |
| # SafeOCR-LabGold |
|
|
| **SafeOCR-LabGold** is the synthetic laboratory-report benchmark used to develop and evaluate the frozen SafeOCR v0.1 verification policy. |
|
|
| ## Dataset summary |
|
|
| The complete frozen split contains: |
|
|
| | Split | Synthetic records | Rendered documents | Critical fields | |
| |---|---:|---:|---:| |
| | Calibration | 12 | 24 | 144 | |
| | Final evaluation | 24 | 48 | 288 | |
| | Total | 36 | 72 | 432 | |
|
|
| Each synthetic record is rendered as a clean document and a deterministic corrupted counterpart. Three controlled templates are used: `classic`, `compact`, and `grid`. Every document contains six laboratory fields. |
|
|
| The paper reports the **48-document / 288-critical-field final evaluation split**. |
|
|
| ## Intended use |
|
|
| Use SafeOCR-LabGold for: |
|
|
| - deterministic clinical-OCR verification research; |
| - regression tests for evidence binding and selective-decision policies; |
| - reproducible demonstrations of review/abstention behavior; |
| - studying automation-versus-review trade-offs under the frozen v0.1 benchmark. |
|
|
| Do **not** use this dataset as evidence of real-world clinical deployment performance. It is synthetic and does not represent the full distribution of real laboratory reports. |
|
|
| ## Frozen result boundary |
|
|
| In the final evaluation split, the primary OCR baseline produced 0/288 observed errors. SafeOCR accepted 209/288 fields with 0/209 observed errors and routed 79 correct fields to review. Because the ungated baseline also had zero observed errors, this benchmark does **not** establish a safety benefit from gating. |
|
|
| ## Data generation and byte-level reproducibility |
|
|
| The benchmark generator is deterministic for a fixed renderer stack: |
|
|
| ```bash |
| python scripts/prepare_hf_labgold.py --output dist/SafeOCR-LabGold |
| ``` |
|
|
| The canonical Hugging Face v0.1 publication package is bound by: |
|
|
| - `manifest_sha256 = 3a3fcdf1e3e045c0b6f8b334457d1e22dbd247a6a556e08235f808143abf5cec`; |
| - Python 3.12.13; |
| - Pillow 12.3.0; |
| - FreeType 2.14.3; |
| - JPEG feature version 8.0; |
| - zlib 1.3.1.zlib-ng; |
| - platform `win32:AMD64`. |
|
|
| SafeOCR's corruption pipeline includes a JPEG round-trip. Pillow can therefore produce different corrupted PNG bytes when its underlying JPEG implementation differs across platforms even when the frozen seeds, case identities, layouts, and policy are unchanged. A package whose manifest hash differs from the canonical value above must not be presented as the byte-identical v0.1 publication artifact. For exact reproduction of the released benchmark, use the published Hugging Face artifacts or a matching renderer stack. |
|
|
| This renderer dependency does not change the frozen evaluation results or claim boundaries; it is a byte-level reproducibility constraint on regenerated corrupted images. |
|
|
| The generator records exact case identity, template, source seed, corruption seed, document hash, page hash, image filename, and truth-region metadata. The canonical root `metadata.jsonl` remains byte-identical to `manifest.jsonl`. |
|
|
| The Hugging Face Dataset Viewer uses a separate derived Parquet layer under `viewer/`. It is generated only after verifying the canonical manifest and every source-image SHA-256. The viewer layer embeds the same image bytes and exposes the frozen metadata as tabular columns, with explicit `calibration` (24 rows) and `evaluation` (48 rows) splits. It is a convenience representation only; `manifest.jsonl` and `images/` remain the audit source of truth. |
|
|
| ## Privacy |
|
|
| All records are synthetic. Synthetic patient names and identifiers are generated by SafeOCR; no real patient data are used. |
|
|
| ## Repository and demo |
|
|
| - Code and frozen evidence: https://github.com/AbdulazizShehri/SafeOCR |
| - Interactive verification playground: https://huggingface.co/spaces/MedScaleAI/SafeOCR |
| - Paper: arXiv identifier will be added at publication |
|
|
| ## Dataset contents |
|
|
| The published repository contains: |
|
|
| - `images/*.png`: canonical synthetic laboratory report pages; |
| - `manifest.jsonl`: canonical audit manifest for the frozen release; |
| - `metadata.jsonl`: root metadata byte-identical to the canonical manifest; |
| - `images/metadata.jsonl`: compatibility ImageFolder metadata adapter; |
| - `viewer/calibration.parquet`: derived 24-row viewer split with embedded canonical images and metadata; |
| - `viewer/evaluation.parquet`: derived 48-row viewer split with embedded canonical images and metadata; |
| - `viewer/viewer_info.json`: viewer-layer counts and SHA-256 identities; |
| - `dataset_info.json`: canonical release counts and SHA-256 identities; |
| - `README.md`: this dataset card. |
|
|
| External datasets used in the paper are not redistributed here. |
|
|
| ## License |
|
|
| The SafeOCR project and generated SafeOCR-LabGold artifacts are released under Apache-2.0 where applicable. Third-party datasets used in the paper retain their own terms and are **not** included in this Hugging Face dataset. |
|
|
