b_number string | page_index int32 | image image | report_text string | license string | n_pages int32 | page_within_report int32 | table_ground_truth string | signals string |
|---|---|---|---|---|---|---|---|---|
b17950454 | 1 | pop 57 ANNUAL REPORT of the MEDICAL OFFICER OF HEALTH for the SOUTH DISTRICT, Comprising the Parishes of All Saints Poplar, and Bromley Saint Leonard. To the Chairman and Members o/ the Board of Works for the Poplar District. Gentlemen, I had the honour to be elected Medical Officer of Health for the parishes of Poplar... | cc-by-nc | 82 | 1 | {"manifest_url": "https://iiif.wellcomecollection.org/presentation/v2/b17950454", "image_service_id": "https://iiif.wellcomecollection.org/image/B17950454_0001.jp2", "size_param": "full", "fmt": "jpg", "license_raw": "http://creativecommons.org/licenses/by-nc/4.0/", "is_collection_child": false, "parent_b_number": null... | ||
b17950454 | 2 | "pop 57 ANNUAL REPORT of the MEDICAL OFFICER OF HEALTH for the SOUTH DISTRICT, Comprising the Parish(...TRUNCATED) | cc-by-nc | 82 | 2 | "{\"manifest_url\": \"https://iiif.wellcomecollection.org/presentation/v2/b17950454\", \"image_servi(...TRUNCATED) | ||
b17950454 | 3 | "pop 57 ANNUAL REPORT of the MEDICAL OFFICER OF HEALTH for the SOUTH DISTRICT, Comprising the Parish(...TRUNCATED) | cc-by-nc | 82 | 3 | "{\"manifest_url\": \"https://iiif.wellcomecollection.org/presentation/v2/b17950454\", \"image_servi(...TRUNCATED) | ||
b17950454 | 4 | "pop 57 ANNUAL REPORT of the MEDICAL OFFICER OF HEALTH for the SOUTH DISTRICT, Comprising the Parish(...TRUNCATED) | cc-by-nc | 82 | 4 | "{\"manifest_url\": \"https://iiif.wellcomecollection.org/presentation/v2/b17950454\", \"image_servi(...TRUNCATED) | ||
b17950454 | 5 | "pop 57 ANNUAL REPORT of the MEDICAL OFFICER OF HEALTH for the SOUTH DISTRICT, Comprising the Parish(...TRUNCATED) | cc-by-nc | 82 | 5 | "{\"manifest_url\": \"https://iiif.wellcomecollection.org/presentation/v2/b17950454\", \"image_servi(...TRUNCATED) | ||
b17950454 | 6 | "pop 57 ANNUAL REPORT of the MEDICAL OFFICER OF HEALTH for the SOUTH DISTRICT, Comprising the Parish(...TRUNCATED) | cc-by-nc | 82 | 6 | "{\"manifest_url\": \"https://iiif.wellcomecollection.org/presentation/v2/b17950454\", \"image_servi(...TRUNCATED) | ||
b17950454 | 7 | "pop 57 ANNUAL REPORT of the MEDICAL OFFICER OF HEALTH for the SOUTH DISTRICT, Comprising the Parish(...TRUNCATED) | cc-by-nc | 82 | 7 | "{\"manifest_url\": \"https://iiif.wellcomecollection.org/presentation/v2/b17950454\", \"image_servi(...TRUNCATED) | ||
b17950454 | 8 | "pop 57 ANNUAL REPORT of the MEDICAL OFFICER OF HEALTH for the SOUTH DISTRICT, Comprising the Parish(...TRUNCATED) | cc-by-nc | 82 | 8 | "{\"manifest_url\": \"https://iiif.wellcomecollection.org/presentation/v2/b17950454\", \"image_servi(...TRUNCATED) | ||
b17950454 | 9 | "pop 57 ANNUAL REPORT of the MEDICAL OFFICER OF HEALTH for the SOUTH DISTRICT, Comprising the Parish(...TRUNCATED) | cc-by-nc | 82 | 9 | "{\"manifest_url\": \"https://iiif.wellcomecollection.org/presentation/v2/b17950454\", \"image_servi(...TRUNCATED) | ||
b17950454 | 10 | "pop 57 ANNUAL REPORT of the MEDICAL OFFICER OF HEALTH for the SOUTH DISTRICT, Comprising the Parish(...TRUNCATED) | cc-by-nc | 82 | 10 | "{\"manifest_url\": \"https://iiif.wellcomecollection.org/presentation/v2/b17950454\", \"image_servi(...TRUNCATED) |
London's Pulse: Medical Officer of Health reports (page images + OCR text)
Page-level scans of the Wellcome Collection London's Pulse Medical Officer of Health (MOH) reports (1848–1972), paired with OCR text, per-report licence, and full provenance. Built for OCR / VLM / document-understanding work on real historical public-health records — dense statistical tables, mixed layouts, century-old print.
Configs
| config | rows | what |
|---|---|---|
default |
391,964 pages / 4,886 reports | every page image + its report's OCR text + licence |
tables |
3,000 table pages / 288 reports | pages that contain a table, each paired with that page's extracted-table ground truth — dual-purpose: page-type/classifier training and an OCR/VLM table-extraction eval set |
from datasets import load_dataset
# full corpus (stream — it's ~110 GB)
ds = load_dataset("biglam/londons-pulse-moh", split="train", streaming=True)
# tables subset (small; image + ground-truth tables)
tab = load_dataset("biglam/londons-pulse-moh", "tables", split="test")
default config — columns
| column | type | description |
|---|---|---|
image |
Image |
the page scan (full native resolution) |
b_number |
string |
Wellcome report id (join key) |
page_index |
int32 |
1-based page (scan) number within the report |
n_pages |
int32 |
total pages in the report |
report_text |
string |
OCR text of the whole report (see caveat) |
license |
string |
normalised image licence (all cc-by-nc here) |
table_ground_truth |
string |
reserved (empty in default; populated in the tables config) |
signals |
string |
JSON provenance (manifest_url, image_service_id, size/format, license_raw, multi-volume linkage) |
report_textis report-level, not page-aligned — the source OCR is a flat per-report dump with no reliable page boundaries, so the same full-report text repeats across every page of that report. Group/dedupe byb_number.
tables config — columns
One row per table page: the page image plus the machine-extracted table(s) Wellcome
published for that page. Use it two ways — (1) as table-labelled training data for
page-type/layout classifiers (the table class is scarce in book-domain sets), and (2) as an
OCR/VLM table-extraction benchmark: feed image, score the model's output against
table_ground_truth.
| column | type | description |
|---|---|---|
image |
Image |
the page scan |
page_type |
string |
"table" |
table_ground_truth |
string |
JSON list of {table_id, csv} — the extracted table(s) on that page |
n_tables_on_page |
int32 |
number of tables on the page |
b_number, page_index, printed_page |
report id, scan index, printed page | |
source_collection, label_source, split, signals |
provenance (label_source="table-export") |
Stratified across all 12 decades; grouped train/val/test split (a report is wholly in one
split — no page leakage). table = a page from which Wellcome extracted ≥1 table (so a page
may also contain prose); printed→scan alignment via IIIF canvas labels, verified, with
composite/multi-section reports excluded.
Ground-truth quality — silver, not gold. The tables are machine-extracted (OCR-based table recognition), not hand-transcribed. Spot-checks against the page images show numeric cell values are highly accurate (often exact), while captions/text labels carry occasional OCR errors (e.g.
classes→olasses,(S.1)→(S.l)), multi-level headers are flattened, and sparse tables have imperfect empty-cell alignment / inconsistent nil markers (-,_, blank). Each CSV is prefixed with an id line, aPage,NNNline, and the table caption. Use as a silver benchmark target: score numeric cells exactly, fuzzy-match text, and don't penalise a model for the GT's own OCR noise. A small hand-corrected gold subset is recommended for headline results.
Provenance & reproducibility
- Images: Wellcome IIIF Presentation v2 API, keyed on each report's b-number, full native resolution.
- Text (
default): the bulkFulltext.zipcorpus from wellcomelibrary.org/moh, joined on b-number. - Tables (
tables): Wellcome'sAll_Report_Tablesexport (~275k machine-extracted tables); each table'sPage,NNNline resolves to a scan image via the manifest's numeric canvas labels. signalsrecords the exact manifest + image service for every page.
Licence
Page images are CC-BY-NC 4.0 (per Wellcome's IIIF manifests; per-row license +
signals.license_raw). The OCR text corpus is CC-BY 4.0; the extracted-tables export is
CC-BY 4.0. Reports whose image licence was not open were excluded. Reuse is
non-commercial, with attribution to Wellcome Collection.
| licence | pages (default) |
|---|---|
| cc-by-nc | 391,964 |
Source & attribution
Wellcome Collection, London's Pulse: Medical Officer of Health reports 1848–1972.
Images: iiif.wellcomecollection.org · Text/tables: wellcomelibrary.org/moh.
Intended uses
Historical OCR/VLM evaluation, document layout analysis, page-type classification, and
structured extraction from century-old public-health tables (disease incidence, mortality).
The tables config is the substrate for an image→table extraction benchmark.
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