Datasets:
image imagewidth (px) 2.49k 2.98k | filename stringlengths 8 8 | objects dict | has_card bool 2
classes | width int32 2.49k 2.98k | height int32 1.68k 1.94k |
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NLS Advocates Library Index Card Detection Dataset
An object detection dataset for identifying index cards on scanned pages from the Advocates Library manuscript collection at the National Library of Scotland.
Dataset Description
This dataset contains 100 annotated scanned page images from the Advocates Library manuscript index card collection. Each image is annotated with bounding boxes marking the location of index cards on the page, enabling training of object detection models to automatically crop cards from full page scans.
The dataset was created as part of an AI consultancy with NLS to build metadata extraction pipelines for library collections.
Features
| Feature | Type | Description |
|---|---|---|
image |
Image | Scanned page image |
filename |
string | Original filename |
objects.bbox |
list of [x, y, w, h] | COCO-format bounding boxes |
objects.category |
ClassLabel | Single class: index_card |
has_card |
bool | Whether the page contains an index card |
width |
int32 | Image width in pixels |
height |
int32 | Image height in pixels |
Pages without index cards (e.g. blank pages, dividers, covers) have has_card: false and empty bounding box lists. This allows the detector to learn what to skip.
Use Cases
- Training object detection models (e.g. YOLO) to identify and crop index cards from scanned pages
- Preprocessing step for downstream metadata extraction pipelines
- Benchmarking detection approaches for similar archival card collections
Trained Model
This dataset was used to train a YOLOv11n model that achieves 99.2% mAP@50-95: NationalLibraryOfScotland/archival-index-card-detector
Related Resources
- Trained detector: NationalLibraryOfScotland/archival-index-card-detector
- Documentation: AI Design Patterns for Information Professionals
Source
National Library of Scotland, Advocates Library manuscript collection. Bounding box annotations created using SAM3 with manual correction.
License
To be determined by the National Library of Scotland.
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