---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': blank
          '1': content
          '2': divider
  - name: source_collection
    dtype: string
  - name: sub_source
    dtype: string
  - name: card_id
    dtype: string
  - name: label_source
    dtype: string
  - name: signals
    dtype: string
  splits:
  - name: train
    num_bytes: 54829425
    num_examples: 392
  - name: validation
    num_bytes: 9957186
    num_examples: 69
  - name: test
    num_bytes: 10397189
    num_examples: 75
  download_size: 75123472
  dataset_size: 75183800
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
task_categories:
- image-classification
tags:
- archives
- document-classification
- glam
- image-classification
- index-cards
- libraries
- ocr-pre-filter
license: cc-by-4.0
pretty_name: Index-card blank / content / divider
---
# Index-card blank / content / divider classifier — dataset

Cropped single **archival index cards** labelled `blank`, `content`, or `divider`, for
training a tiny CPU pre-filter that skips blank/divider cards before expensive VLM metadata
extraction in card-catalogue digitisation pipelines.

Two collections: **Boston Public Library (BPL)** FRC shelf-list cards and **National Library
of Scotland (NLS)** Advocates Library cards. Styles differ, so evaluate per collection.

## How it was made (provenance)

AI-bootstrapped → agent-verified, no from-scratch hand labelling:

1. **Weak signals fused** into labels:
   - **NuExtract3 `card_type`** (BPL labelled sample): bibliographic→content,
     shelf_divider→divider, null→blank.
   - **Ink-density** with punch-hole removal (connected-component analysis) — the
     cross-collection blank detector. Calibrated on the labelled sample: 100% blank recall /
     96% content recall at threshold 0.005. Used to **harvest** extra blanks from unlabelled
     BPL shelf-list drawers.
   - **YOLO card-detector box-count** (`NationalLibraryOfScotland/archival-index-card-detector`)
     validated as an oracle: reliable on NLS (100% separation), noisy on BPL crops (31% of
     blanks falsely fire) → used as a primary signal for NLS, corroborating only for BPL.
   - **NLS `has_card`** + bbox: crop content cards from pages.
2. **Agreement → auto-accept; disagreement → routed to human.**
3. **Gold (test) split human-verified** per collection (not auto-thresholded), so it measures
   generalisation rather than pipeline self-consistency.

## Composition

| collection | label | train | val | gold | total |
|---|---|---|---|---|---|
| bpl | blank | 209 | 37 | 30 | 276 |
| bpl | content | 122 | 21 | 30 | 173 |
| bpl | divider | 32 | 6 | 0 | 38 |
| nls | content | 29 | 5 | 15 | 49 |

`label_source` records how each label was derived (`auto:nuextract+ink`, `auto:ink-density`,
`auto:has_card+box-count`); `signals` is a JSON audit of the raw per-card signal values.

## Intended use & limitations

- **Use**: train a tiny `transformers` image classifier as a `--skip-blank` pre-filter.
- **`divider`** is captured but held out of the binary v1 model (fast-follow 3-class).
- **NLS blank gap (v1)**: NLS contributes content cards only — no clean blank *fronts* exist
  in the source (NLS no-card examples are empty *pages*, a different visual domain, and the
  detector rejects blank fronts). So v1 reports NLS *content* precision/recall; NLS blank
  recall awaits a v2 harvest of NLS blank fronts.
- **Punch-hole/smudge**: blank cards carry a punch-hole and sometimes show-through smudges;
  these are deliberately included so the model learns a small dark blob ≠ content.

## Use this for your own collection

Point an agent at your card images; bootstrap labels from whatever weak signals you have (a
card detector, an existing VLM `card_type` field, an ink-density heuristic with punch-hole
handling); auto-accept agreements and human-correct the rest; hold out a small verified gold
set; then train a tiny classifier and add your rows tagged by `source_collection`. Full
workflow: [`data-centric-model-dev`](https://huggingface.co/small-models-for-glam).

## Sources

- BPL FRC shelf-list cards — Internet Archive `bplfrcshelflistcards`.
- `davanstrien/bpl-shelf-list-nuextract3` (NuExtract3 weak labels).
- `NationalLibraryOfScotland/nls-index-cards-object-detection`.
- Detector: `NationalLibraryOfScotland/archival-index-card-detector`.