Purpose: Historical newspaper collections provide a wealth of information about the past. Although the digitization of these collections significantly improves their accessibility, a large portion of digitized historical newspaper collections, such as those of KBR, the Royal Library of Belgium, are not yet searchable at article-level. However, recent developments in AI-based research methods, such as document layout analysis, have the potential for further enriching the metadata to improve the searchability of these historical newspaper collections. This paper aims to discuss the aforementioned issue.Design/methodology/approach: In this paper, the authors explore how existing computer vision and machine learning approaches can be used to improve access to digitized historical newspapers. To do this, the authors propose a workflow, using computer vision and machine learning approaches to (1) provide article-level access to digitized historical newspaper collections using document layout analysis, (2) extract specific types of articles (e.g. feuilletons – literary supplements from Le Peuple from 1938), (3) conduct image similarity analysis using (un)supervised classification methods and (4) perform named entity recognition (NER) to link the extracted information to open data.Findings: The results show that the proposed workflow improves the accessibility and searchability of digitized historical newspapers, and also contributes to the building of corpora for digital humanities research. The AI-based methods enable automatic extraction of feuilletons, clustering of similar images and dynamic linking of related articles.Originality/value: The proposed workflow enables automatic extraction of articles, including detection of a specific type of article, such as a feuilleton or literary supplement. This is particularly valuable for humanities researchers as it improves the searchability of these collections and enables corpora to be built around specific themes. Article-level access to, and improved searchability of, KBR's digitized newspapers are demonstrated through the online tool (https://tw06v072.ugent.be/kbr/).

Newspapers contain a wealth of historical information in the form of articles and illustrations. Libraries and cultural heritage institutions have been digitizing their collections for decades to enable web-based access to and retrieval of information. A number of challenges arise when dealing with digitized collections, such as those of KBR, the Royal Library of Brussels (used in this study), which contain only page-level metadata, making it difficult to extract information from specific contexts. A context-aware search relies heavily on metadata enhancement. Therefore, when using metadata at the page level, it is even more challenging to geolocalize less-known landmarks. To overcome this challenge, we have developed a pipeline for geolocalization and visualization of historical photographs. The first step of this pipeline consists of converting page-level metadata to article-level metadata. In the next step, all articles with building images were classified based on image classification algorithms. Moreover, to correctly geolocalize historical photographs, we propose a hybrid approach that uses both textual metadata and image features. We conclude this research paper by addressing the challenge of visualizing historical content in a way that adds value to humanities research. It is noteworthy that a number of historical urban scenes are visualized using rephotography, which is notoriously challenging to get right. This study serves as an important step towards enriching historical metadata and facilitating cross-collection linkages, geolocalization, and the visualization of historical newspaper images. Furthermore, the proposed methodology is generic and can be used to process untagged photographs from social media, including Flickr and Instagram.