--- annotations_creators: - machine-generated language_creators: - found license: - cc0-1.0 size_categories: - 1M the book this image came from fname = "002543810_3_Dzieje Narodu Polskiego...jpg" record_id = fname.split("_")[0] # "002543810" ``` The 4% that don't match are books present in the image deposit but absent from the OCR release. The join is at **book** level, not page level — `fname` encodes a page position, but it is not guaranteed to align with the `pg` column in the OCR corpus, so treat page-level alignment as something to verify rather than assume. ## What was selected, and by whom The date range is not a sampling frame. The corpus is what the British Library and Microsoft chose to digitise from what the Library had acquired from what happened to survive — three selection layers stacked before any image reached this dataset — and digitisation programmes of this era were driven partly by preservation priorities and out-of-copyright status rather than by any attempt at representativeness. The 19th century dominates. Treat the collection as a record of what a large British institution digitised in the early 2010s, not as a sample of printed illustration. Colonial-era publishing is heavily represented, and images carry the depictions, captions and categories of their period. Nothing here has been reviewed for offensive content. ## Related datasets The OCR text from the same digitisation programme is [`biglam/blbooks-parquet`](https://huggingface.co/datasets/biglam/blbooks-parquet) (14,011,953 pages). ## Licensing The images were published on Flickr Commons under **no known copyright restrictions** — the British Library placed them in the public domain, and the underlying works are out of copyright. Tagged `cc0-1.0` here for machine-readability; the deposit's own statement is the Public Domain Mark. No licence attaches to the images themselves that would restrict reuse. Attribution to the British Library is expected practice rather than a legal condition. ## Credit Mirrored and repackaged by [Daniel van Strien](https://huggingface.co/davanstrien) (Machine Learning Librarian, Hugging Face); all source data created by the British Library, digitised in partnership with Microsoft and released by British Library Labs. The four image types were deposited as four separate records on the British Library Research Repository (bl.iro.bl.uk), which is intermittently unavailable — this dataset is the more reliable route to the images. Against the counts those deposits state, this dataset is a handful of images short: 6 fewer plates, 1 fewer medium, 13 fewer covers. The shortfall predates this repackaging and no cause has been established for it. Point of contact for the original deposit: labs@bl.uk Maintenance: **Limited Maintenance** — this is a mirror of a static 2014 deposit and is not expected to change. ## Citation ```bibtex @misc{bl_labs_book_images, title = {Digitised Books. c. 1510 - c. 1900. JPG}, author = {{British Library Labs}}, year = {2014}, publisher = {British Library}, howpublished = {\url{https://bl.iro.bl.uk/}} } ``` ## SigLIP2 embeddings (`siglip2_embeddings` config) Every image in this dataset, embedded with [google/siglip2-so400m-patch16-256](https://huggingface.co/google/siglip2-so400m-patch16-256) (1152-d float32, images resized to 256x256 before encoding — the model's own preprocessing shape). One split per image config; rows are sorted to match the source parquet exactly, so **row N of split `plates` is row N of config `plates`**. Each row also carries `source_filename` and `file_row_number`, the direct coordinates of its image in this repository. ```python from datasets import load_dataset emb = load_dataset("biglam/british-library-book-images", "siglip2_embeddings", split="plates") ``` Because SigLIP2 is a dual encoder, text queries embed into the same space — encode a phrase with the model's text tower and rank rows by cosine similarity for free-text search over the collection. Notes: embeddings were produced with vLLM serving SigLIP2 as a pooling model on Hugging Face Jobs; all 1,080,814 images are covered, one embedding each. 768 image pairs (1,536 filenames) are near-duplicates differing only in title spacing (same system number/volume/page/block) — these are distinct rows here, as in the source configs. ## Crop masks (`crop_masks` config) Model-predicted instance masks and bounding boxes for 1,019,266 images — every image in the `embellishments`, `plates` and `medium` configs (`covers` is excluded: the model cannot abstain, and covers are overwhelmingly not illustrations). The ABBYY-derived crops in this dataset are loose — a `medium` crop typically carries lines of body text above the art and a printed caption below — and these masks are the tightening layer: 3,022,916 instances, one row per image, joinable on `fname`. Each row carries `objects` (index-aligned lists: `bbox` as `[x, y, w, h]` in source-frame pixels, `score`, `area`, `rectangularity`) and `masks_rle` (a JSON string of COCO RLE dicts in the same order; the RLE frame is the full-resolution source image, `src_width` x `src_height`). **Filter by `score` before display use.** Predictions were kept down to a deliberately low threshold of 0.10 so that consumers can choose their own operating point, and 59.7% of instances score below 0.3 — at 0.10, dense pages can carry dozens of low-confidence instances. `score >= 0.3` is a sensible display default; the confident singletons that dominate the corpus (76% of images have exactly one instance) are unaffected. ```python import json from datasets import load_dataset from pycocotools import mask as maskutil masks = load_dataset("biglam/british-library-book-images", "crop_masks", split="plates") row = masks[0] rles = json.loads(row["masks_rle"]) keep = [i for i, s in enumerate(row["objects"]["score"]) if s >= 0.3] m = maskutil.decode(rles[keep[0]]) # HxW numpy array, source frame ``` To join masks against the image configs' metadata, DuckDB reads both sides straight off the Hub with column pruning — no image bytes are fetched unless you select the `image` column: ```python import duckdb duckdb.sql(""" SELECT i.fname, i.date, m.objects, m.masks_rle FROM 'hf://datasets/biglam/british-library-book-images/plates/*.parquet' i JOIN 'hf://datasets/biglam/british-library-book-images/crop-masks/plates-*.parquet' m USING (fname) """) ``` Provenance: predicted by [davanstrien/bl-crop-tighten-rfdetrseg-clip10](https://huggingface.co/davanstrien/bl-crop-tighten-rfdetrseg-clip10), an RF-DETR-Seg student distilled from [Falcon-Perception](https://huggingface.co/tiiuae/Falcon-Perception-0.6B) weak labels on 8,400 of these crops. On a 40-image random human check, 97.4% of the student's predictions were judged acceptable — matching its teacher, at roughly 12x the throughput. Every row is stamped with `model_id` and `model_sha`.