---
language:
  - en
license: cc0-1.0
license_details: "Mixed across collections — see Source Data. NLS data is unrestricted use under NLS terms; navy-nurse-corps is US Government public-domain; bpl-card-catalog and rubenstein-manuscript-catalog are CC0-1.0."
tags:
  - object-detection
  - index-cards
  - archives
  - glam
  - cultural-heritage
  - libraries
  - manuscripts
annotations_creators:
  - machine-generated
  - expert-generated
language_creators:
  - expert-generated
pretty_name: Archival Index Card Detection — mixed collections
size_categories:
  - 1K<n<10K
source_datasets:
  - NationalLibraryOfScotland/nls-index-cards-object-detection
  - biglam/index-cards-navy-nurse-corps
  - biglam/bpl-card-catalog
  - biglam/rubenstein-manuscript-catalog
task_categories:
  - object-detection
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  config_name: default
  features:
    - name: image
      dtype: image
    - name: source_collection
      dtype: string
    - name: source_repo
      dtype: string
    - name: source_row_id
      dtype: int64
    - name: source_url
      dtype: string
    - name: text
      dtype: string
    - name: objects
      struct:
        - name: bbox
          sequence:
            sequence: float32
            length: 4
        - name: category
          sequence:
            class_label:
              names:
                "0": card
        - name: box_source
          sequence: string
  splits:
    - name: train
      num_bytes: 330329844
      num_examples: 1425
  download_size: 330102842
  dataset_size: 330329844
---

# Dataset Card for Archival Index Card Detection — mixed collections

A training dataset for object detection of index cards in archival scans. Combines four publicly-released collections — NLS Advocates Library single-card pages, US Navy Nurse Corps multi-card biographical sheets, Boston Public Library catalog cards, and Duke Rubenstein manuscript catalog cards — into a single object-detection schema.

## Dataset Details

### Dataset Description

1,425 archival scans across four institutional collections. Constructed to extend the existing single-card NLS detector ([`NationalLibraryOfScotland/archival-index-card-detector`](https://huggingface.co/NationalLibraryOfScotland/archival-index-card-detector)) to handle multi-card scans and pre-cropped single-card images from other institutions, without regressing on the original NLS distribution.

- **Curated by:** [Daniel van Strien](https://huggingface.co/davanstrien), Machine Learning Librarian, Hugging Face
- **Language(s):** en (card content; English-only)
- **License:** Mixed — see Source Data table below. Effectively unrestricted re-use for academic / research / non-commercial purposes; check the source dataset for commercial reuse.

### Dataset Sources

- **Repository:** https://huggingface.co/datasets/small-models-for-glam/index-card-detection-v3
- **Trained model:** https://huggingface.co/small-models-for-glam/index-card-detector-v4
- **Demo Space:** https://huggingface.co/spaces/small-models-for-glam/index-card-detector

## Uses

### Direct Use

Train an object detector to find archival index cards on:
- Full archival page scans with one or more cards (NLS-style multi-card pages)
- Multi-card sheets (2–9 cards arranged on a backing sheet, e.g. Navy biographical records)
- Pre-cropped single-card images (BPL / Rubenstein style)

The accompanying YOLO26n checkpoint at `small-models-for-glam/index-card-detector-v4` is fine-tuned from the NLS v1 baseline on this data.

### Out-of-Scope Use

- **Not for OCR.** Detection only — bbox tells you *where* the card is, not *what it says*. Pair with a downstream OCR/VLM model (NuExtract3, Qwen-VL, etc.).
- **Not for content classification.** Single class `card` — does not distinguish card type, content, or blank/content.
- **English-only.** Cards in other languages may not generalise.
- **Not a benchmark dataset.** Train/val split is automatic and stratified per collection; not held back as a community evaluation set.

## Dataset Structure

One row per image:

| Column | Type | Description |
|---|---|---|
| `image` | `Image` | RGB scan, original resolution |
| `source_collection` | `string` | One of `nls-advocates`, `navy-nurse-corps`, `bpl`, `rubenstein` |
| `source_repo` | `string` | Source HF dataset id |
| `source_row_id` | `int64` | Index in the source dataset |
| `source_url` | `string` | Link to original item (when present in source) |
| `text` | `string` | OCR or filename from source (preserved as-is) |
| `objects.bbox` | `list[[float, 4]]` | xywh in original pixel coordinates |
| `objects.category` | `ClassLabel(["card"])` | Single class; always `0` |
| `objects.box_source` | `list[string]` | Provenance per box: `nls-original` \| `sam3+human` \| `auto-full` |

Per-collection breakdown:

| Collection | Rows | Boxes | Avg boxes/row | Avg image size |
|---|---|---|---|---|
| nls-advocates | 100 | ~155 | 1.55 (incl. some has_card=False) | ~2900×1800 |
| navy-nurse-corps | 25 | ~85 | 3.4 | varied (3K×4K to 10K×6K) |
| bpl | 800 | 800 | 1.0 | ~1175×710 |
| rubenstein | 500 | 500 | 1.0 | ~1555×1100 |
| **Total** | **1,425** | **~1,540** | | |

## Dataset Creation

### Curation Rationale

The existing NLS detector (v1) was trained on 100 NLS Advocates Library pages and reached 99.1% mAP@50:95 on that distribution, but had not been tested on multi-card scans or cards from other institutions. This dataset extends v1's training distribution while preserving the NLS samples to prevent catastrophic forgetting.

### Source Data

#### Data Collection and Processing

Four source collections, all publicly hosted on the Hugging Face Hub:

| Collection | Source | n used | Label provenance | License (source) |
|---|---|---|---|---|
| NLS Advocates Library | `NationalLibraryOfScotland/nls-index-cards-object-detection` | 100 (all) | Original NLS bboxes (10 has_card=False negatives included) | Per source repo |
| US Navy Nurse Corps | `biglam/index-cards-navy-nurse-corps` | 25 (all) | SAM3 bootstrap + human review/correction | Public domain (US Govt) |
| Boston Public Library | `biglam/bpl-card-catalog` | 800 (first rows) | `bbox = whole image` (auto, pre-cropped) | CC0-1.0 |
| Duke Rubenstein | `biglam/rubenstein-manuscript-catalog` | 500 (first rows) | `bbox = whole image` (auto, pre-cropped) | CC0-1.0 |

Sloane Catalogues (`biglam/sloane-catalogues`) was considered and **excluded** — those scans are two-page manuscript catalog book spreads, not index cards.

#### Who are the source data producers?

Original archival cards were created by librarians and archivists at the four source institutions over the late 19th / 20th century. Digitisation by each institution and/or via Internet Archive.

### Annotations

#### Annotation process

Three different paths into the same object-detection schema:

1. **NLS Advocates (100 imgs):** original bboxes from the NLS detector v1 training set, passed through unchanged. Includes 10 has_card=False pages as negatives.

2. **Navy Nurse Corps (25 imgs):** bootstrap with Meta's SAM3 ([`uv-scripts/sam3`](https://huggingface.co/datasets/uv-scripts/sam3), `--class-name "card" --confidence-threshold 0.15` on an A100). Outputs were reviewed and corrected in a custom single-page HTML bbox editor. One systematic SAM3 failure (an "envelope" box wrapping all cards on tall multi-card scans, ~95–98% image area) was auto-filtered before review. Final per-image: ~3.4 cards on average.

3. **BPL + Rubenstein (1,300 imgs):** auto-labelled `bbox = [1, 1, W-2, H-2]` based on the structural fact that source images are pre-cropped single cards filling the frame. No human review pass.

The `objects.box_source` column records which path produced each box.

#### Who are the annotators?

- NLS: NLS internal team
- Navy: [Daniel van Strien](https://huggingface.co/davanstrien) (review and correction over SAM3 bootstrap)
- BPL + Rubenstein: rule-based (no human pass)

#### Personal and Sensitive Information

Cards contain names, addresses, and biographical details of individuals (Navy Nurse Corps records in particular). All source datasets are from publicly-released collections — personal information is in the public archival record by virtue of original institutional release. Downstream users should respect each source institution's usage policies.

## Bias, Risks, and Limitations

- **English-only.** Cards in other languages are not represented.
- **US/UK archival conventions only.** Card stocks, layouts, and aesthetics outside that tradition (e.g. continental European library catalogs, East Asian indices, hand-coloured cards) are out-of-distribution.
- **Multi-card variety concentrated in 25 navy images.** Multi-card scans with very different layouts (e.g., card grids of 12+, severely overlapping cards) may not generalise.
- **No true negative non-card examples beyond the 10 NLS background pages.** Newspaper clippings, photographs, and book pages are not in the training set; models trained on this may over-predict on those inputs. Pair with downstream filtering or train v5 with additional negatives.
- **BPL + Rubenstein labels are rule-based, not human-verified.** `bbox = whole image` is a reasonable approximation for pre-cropped cards but slight cropping noise around the edges is not captured. Tight pixel-accuracy downstream tasks may want a hand-labelled refresh.

### Recommendations

- For tight cropping or pixel-accurate downstream OCR, validate model output on a small held-out set before bulk processing.
- For archives mixing cards with other content, add negatives or apply a downstream non-card filter.
- For cards in other languages, fine-tune with additional samples from those traditions before deployment.

## Citation

**BibTeX:**

```bibtex
@dataset{vanstrien_archival_index_cards_v3_2026,
  author    = {van Strien, Daniel},
  title     = {Archival Index Card Detection — mixed collections (v3)},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/small-models-for-glam/index-card-detection-v3}
}
```

## Dataset Card Authors

[Daniel van Strien](https://huggingface.co/davanstrien)

## Dataset Card Contact

Open an issue on the [dataset repo](https://huggingface.co/datasets/small-models-for-glam/index-card-detection-v3/discussions) or contact [@davanstrien](https://huggingface.co/davanstrien).