above-mentioned challenges, the digitized versions of newspapers, including those from KBR, the Royal Library of Belgium, contain only page-level metadata, making it difficult to extract information related to a particular topic. Access to KBR's digitized newspapers is available through the BelgicaPress platform [13]. When someone searches for a term with a keyword, all pages related to that term are displayed. A newspaper page contains numerous articles on different topics. This makes the page-level search less valuable and can lead to irrelevant results. Therefore, it is necessary to improve the metadata to allow a context-aware search in these historical collections. The next step after digitization and providing access to collections is enrichment. Metadata enrichment is the process of adding extra information about the data. The additional information can be the name of an event, the date of publication, the article number, the author of an article, the picture description, or the location where a picture was taken. The metadata extracted from these newspapers can enhance the collection and thus make it more accessible. In order to explore the collections, traditional image processing techniques are used to segment the articles so that users can perform context-aware searches. Context-aware cultural heritage applications are expected to improve searchability, user interaction, and engagement with historical datasets [14, 15]. Computer vision and machine learning techniques are used to further enrich metadata to facilitate article-level search, cross collection linking, named entity recognition, and enable the user to perform context-aware queries on the collections. Based on these advanced computational methods, researchers now have the opportunity to create full-featured virtual interfaces [16, 17, 18] for digital humanities research. _NewspAlper_[19], an AI-based metadata enrichment solution for historical newspaper collections, was developed to help humanities researchers extract information from vast collections relevant to specific research scenarios. This tool led to the development of corpora on specific research themes. A corpus could be a collection of articles related to \"Strikes in Belgium\" or a bundle of pictures related to the \"style of buildings\". The next step is the analysis of the data corpus after it has been compiled according to specific research scenarios. Among cultural heritage materials, text analysis [20, 21] has been the main focus of research for decades. In the era of increasing data availability, technologies such as linked open data and the semantic web are becoming more popular for querying and exploring textual information [22, 23, 24]. The lack of focus on image analysis could be due to the fact that images are usually in halftone format [25] and are difficult to process manually. Even with advanced computational methods, finding features and correlations between different images still remains a challenge. Image data enrichment also has significant value in the same way as text enrichment improves the accessibility and searchability of historical collections [26]. The geolocalization of these historical collections is one of the essential steps to enrich the metadata of historical images. Recent studies show that most geolocalization algorithms are only image-based solutions and work only for well-known landmarks [27]. In this way, many historical images that are less known in the international community but have significant value to a local community cannot be geolocalized. Furthermore, some geolocalization methods are trained on color images and fail to geolocate black and white historical image collections. The main goal of this research was to present a state-of-the-art solution for geolocalizing much more than famous landmarks. Contextual information related to an image is vital for geolocating untagged images.