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This article provides an account of the making of KBLab, the data lab at the National Library of Sweden (KB). The first part of the article offers an evaluative discussion of the work involved in establishing KBLab as both a physical and a digital site for researchers to use KB’s digital collections at previously unimaginable scales. Beyond explaining how the lab aligns with KB’s broader mission as a national library, we also elaborate upon the design of the technical setup and the processes of research coordination that the operation of a library lab presumes. The second part discusses how KBLab has deployed the library’s collections as data to produce high quality Swedish AI models, which constitute a significant new form of digital research infrastructure. We situate this development work in the context of uneven AI coverage for smaller languages, and consider how the lab’s models have contributed to the making of important AI infrastructure for the Swedish language. The conclusion raises the possibilities and challenges involved in continuing the type of library-based AI development we have initiated at KBLab.

This article provides an account of the making of KBLab, the data lab at the National Library of Sweden (KB). The first part of the article offers an evaluative discussion of the work involved in establishing KBLab as both a physical and a digital site for researchers to use KB’s digital collections at previously unimaginable scales. Beyond explaining how the lab aligns with KB’s broader mission as a national library, we also elaborate upon the design of the technical setup and the processes of research coordination that the operation of a library lab presumes. The second part discusses how KBLab has deployed the library’s collections as data to produce high quality Swedish AI models, which constitute a significant new form of digital research infrastructure. We situate this development work in the context of uneven AI coverage for smaller languages, and consider how the lab’s models have contributed to the making of important AI infrastructure for the Swedish language. The conclusion raises the possibilities and challenges involved in continuing the type of library-based AI development we have initiated at KBLab.

This article provides an account of the making of KBLab, the data lab at the National Library of Sweden (KB). The first part of the article offers an evaluative discussion of the work involved in establishing KBLab as both a physical and a digital site for researchers to use KB’s digital collections at previously unimaginable scales. Beyond explaining how the lab aligns with KB’s broader mission as a national library, we also elaborate upon the design of the technical setup and the processes of research coordination that the operation of a library lab presumes. The second part discusses how KBLab has deployed the library’s collections as data to produce high quality Swedish AI models, which constitute a significant new form of digital research infrastructure. We situate this development work in the context of uneven AI coverage for smaller languages, and consider how the lab’s models have contributed to the making of important AI infrastructure for the Swedish language. The conclusion raises the possibilities and challenges involved in continuing the type of library-based AI development we have initiated at KBLab.

This article provides an account of the making of KBLab, the data lab at the National Library of Sweden (KB). The first part of the article offers an evaluative discussion of the work involved in establishing KBLab as both a physical and a digital site for researchers to use KB’s digital collections at previously unimaginable scales. Beyond explaining how the lab aligns with KB’s broader mission as a national library, we also elaborate upon the design of the technical setup and the processes of research coordination that the operation of a library lab presumes. The second part discusses how KBLab has deployed the library’s collections as data to produce high quality Swedish AI models, which constitute a significant new form of digital research infrastructure. We situate this development work in the context of uneven AI coverage for smaller languages, and consider how the lab’s models have contributed to the making of important AI infrastructure for the Swedish language. The conclusion raises the possibilities and challenges involved in continuing the type of library-based AI development we have initiated at KBLab.

This article provides an account of the making of KBLab, the data lab at the National Library of Sweden (KB). The first part of the article offers an evaluative discussion of the work involved in establishing KBLab as both a physical and a digital site for researchers to use KB’s digital collections at previously unimaginable scales. Beyond explaining how the lab aligns with KB’s broader mission as a national library, we also elaborate upon the design of the technical setup and the processes of research coordination that the operation of a library lab presumes. The second part discusses how KBLab has deployed the library’s collections as data to produce high quality Swedish AI models, which constitute a significant new form of digital research infrastructure. We situate this development work in the context of uneven AI coverage for smaller languages, and consider how the lab’s models have contributed to the making of important AI infrastructure for the Swedish language. The conclusion raises the possibilities and challenges involved in continuing the type of library-based AI development we have initiated at KBLab.

How can new AI methods be used to improve the searchability and accessibility of visual heritage collections? We’ve taken advantage of the possibilities opened up by multimodal AI models to produce a demo showcasing these capabilities via the example of postcards. Here we explain something about the project behind this and encourage you to explore our image search demo! https://lab.kb.se/bildsok

The National Library of Sweden uses optical character recognition software to digitize their collections of historical newspapers. The purpose of such software is first to automatically segment text and images from scanned newspaper pages, and second to read the contents of the identified text regions. While the raw text is often digitized successfully, important contextual information regarding whether the text constitutes for example a header, a section title or the body text of an article is not captured. These characteristics are easy for a human to distinguish, yet they remain difficult for a machine to recognize. The main purpose of this thesis is to investigate how well section titles in the newspaper Svenska Dagbladet can be classified by using so called image embeddings as features. A secondary aim is to examine whether section titles become harder to classify in older newspaper data. Lastly, we explore if manual annotation work can be reduced using the predictions of a semi-supervised classifier to help in the labeling process. Results indicate the use of image embeddings help quite substantially in classifying section titles. Datasets from three different time periods: 1990-1997, 2004-2013, and 2017 and onwards were sampled and annotated. The best performing model (Xgboost) achieved macro F1 scores of 0.886, 0.936 and 0.980 for the respective time periods. The results also showed classification became more difficult on older newspapers. Furthermore, a semi-supervised classifier managed an average precision of 83% with only single section title examples, showing promise as way to speed up manual annotation of data.

The National Library of Sweden uses optical character recognition software to digitize their collections of historical newspapers. The purpose of such software is first to automatically segment text and images from scanned newspaper pages, and second to read the contents of the identified text regions. While the raw text is often digitized successfully, important contextual information regarding whether the text constitutes for example a header, a section title or the body text of an article is not captured. These characteristics are easy for a human to distinguish, yet they remain difficult for a machine to recognize. The main purpose of this thesis is to investigate how well section titles in the newspaper Svenska Dagbladet can be classified by using so called image embeddings as features. A secondary aim is to examine whether section titles become harder to classify in older newspaper data. Lastly, we explore if manual annotation work can be reduced using the predictions of a semi-supervised classifier to help in the labeling process. Results indicate the use of image embeddings help quite substantially in classifying section titles. Datasets from three different time periods: 1990-1997, 2004-2013, and 2017 and onwards were sampled and annotated. The best performing model (Xgboost) achieved macro F1 scores of 0.886, 0.936 and 0.980 for the respective time periods. The results also showed classification became more difficult on older newspapers. Furthermore, a semi-supervised classifier managed an average precision of 83% with only single section title examples, showing promise as way to speed up manual annotation of data.

This article provides an account of the making of KBLab, the data lab at the National Library of Sweden (KB). The first part of the article offers an evaluative discussion of the work involved in establishing KBLab as both a physical and a digital site for researchers to use KB’s digital collections at previously unimaginable scales. Beyond explaining how the lab aligns with KB’s broader mission as a national library, we also elaborate upon the design of the technical setup and the processes of research coordination that the operation of a library lab presumes. The second part discusses how KBLab has deployed the library’s collections as data to produce high quality Swedish AI models, which constitute a significant new form of digital research infrastructure. We situate this development work in the context of uneven AI coverage for smaller languages, and consider how the lab’s models have contributed to the making of important AI infrastructure for the Swedish language. The conclusion raises the possibilities and challenges involved in continuing the type of library-based AI development we have initiated at KBLab.