# Visual Navigation of Digital Libraries: Retrieval and Classification of Images in the National Library of Norway's Digitised Book Collection

arXiv: https://arxiv.org/abs/2410.14969 · HTML: https://mlsanity.com/paper/2410.14969/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 19 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Multimodal datasets: misogyny, pornography, and malignant stereotypes](https://mlsanity.com/paper/2110.01963/) (2021)
- [Recent Advance in Content-based Image Retrieval: A Literature Survey](https://mlsanity.com/paper/1706.06064/) (2017)
- [MapReader: A Computer Vision Pipeline for the Semantic Exploration of Maps at Scale](https://mlsanity.com/paper/2111.15592/) (2021)
- [State of the Art: Image Hashing](https://mlsanity.com/paper/2108.11794/) (2021)
- [Is Medieval Distant Viewing Possible? : Extending and Enriching Annotation of Legacy Image Collections using Visual Analytics](https://mlsanity.com/paper/2208.09657/) (2022)

## Built on by (most cited first, 1 of 1)

- [DEMUN: Fast and accurate discovery of music notation in very large collections](https://mlsanity.com/paper/2606.31956/) (2026)

## Abstract (arXiv)

Digital tools for text analysis have long been essential for the searchability and accessibility of digitised library collections. Recent computer vision advances have introduced similar capabilities for visual materials, with deep learning-based embeddings showing promise for analysing visual heritage. Given that many books feature visuals in addition to text, taking advantage of these breakthroughs is critical to making library collections open and accessible. In this work, we present a proof-of-concept image search application for exploring images in the National Library of Norway's pre-1900 books, comparing Vision Transformer (ViT), Contrastive Language-Image Pre-training (CLIP), and Sigmoid loss for Language-Image Pre-training (SigLIP) embeddings for image retrieval and classification. Our results show that the application performs well for exact image retrieval, with SigLIP embeddings slightly outperforming CLIP and ViT in both retrieval and classification tasks. Additionally, SigLIP-based image classification can aid in cleaning image datasets from a digitisation pipeline.