BZK-LE: A Dataset for Zero-Shot and Few-Shot Archival Document Layout Classification
Year: 2026
Type: conference-paper
Abstract: Document image classification (DIC) for historical archival documents remains challenging due to heterogeneous layouts, degraded documents, and class imbalance. Existing benchmarks mainly target contemporary documents and typically assume access to large amounts of annotated training data, which is often unrealistic in archival settings where labeled data is limited. In this work, BZK-LE is introduced as a dataset for evaluating existing DIC models under zero-shot and few-shot settings. It consists of 9,144 index cards and their corresponding layout class labels, from the Bundeszentralkartei (BZK), documenting post-war compensation proceedings related to Nazi persecution. The dataset comprises 22 distinct pre-printed layout types and preserves the natural class imbalance. The images exhibit typical archival artifacts such as handwritten and typewritten text, annotations, stamps, and spatial misalignment. Baseline results using state-of-the-art layout-aware DIC models are reported on both versions. The datasets are available under https://zenodo.org/records/18790631 .
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,392
OpenAlex ID: W7204457002