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README.md

@@ -41,4 +41,81 @@ configs:
     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`.