This paper presents Project X (name anonymized for review), an ongoing initiative to compile a multilingual, comparable, annotated, translated, and interoperable collection of European historical newspaper corpora. Spanning 17 countries and covering 15 languages, the project addresses a key shortcoming of existing newspaper resources: their lack of interoperability, which limits cross-lingual and transnational research. Building on the infrastructure and experience of the ParlaMint projects, the project adapts established encoding guidelines, validation workflows, and open-source tools to historical newspaper data. We outline the overall project architecture, the corpus encoding scheme, and the GitHub-based framework supporting collaborative development and quality control. The paper further describes the sample linguistic annotation pipeline, including OCR correction, text normalisation, and annotation within the Universal Dependencies framework, with attention to challenges posed by historical language varieties. The resulting FAIR, openly available corpora are intended to support comparative, diachronic research across the humanities and social sciences.

This article presents the Polish contribution to the PressMint project, a CLARIN initiative aimed at creating a pan-European, multilingual corpus of historical newspapers. The Polish dataset consists of three subcorpora spanning 110 years (1830–1939). The first two components are drawn from the Microcorpus of Nineteenth-Century Polish (its short press texts and journalistic texts subcorpora), each containing 200 samples of brief news items and journalistic articles from diverse periodicals. The third component, the InterWar Corpus, covers the period 1918–1939 and comprises approximately 6.5 million words from complete newspaper issues, representing the territory of the interwar Republic of Poland. The authors argue for the scholarly value of historical press, highlighting its precise chronological dating as a key advantage for diachronic research despite challenges such as heterogeneous content and anonymous authorship. The conversion pipeline maps source metadata to a standardized TEI format and enriches texts with linguistic annotation using the Hydra NLP tool, providing lemmatization, part-of-speech tagging (mapped to Universal Dependencies), dependency parsing, and named entity recognition. The resulting openly accessible dataset enables cross-linguistic comparison and distant reading of historical press materials on a European scale.

PressMint QuickCheck is a lightweight, reproducible readiness diagnostic for historical newspaper collections. Given a candidate dataset (ZIP export or IIIF manifests), it detects which components are present, identifies interoperability-critical metadata gaps, and applies lightweight OCR sanity checks. It produces three standardised artefacts: a human-readable readiness report, a minimal normalised manifest (CSV), and a tentative v1 scorecard (suitability_score 0-4) for prioritisation across collections. The workflow is delivered as a Colab-first notebook (no installation required). A key design decision treats content_language and metadata_language declarations as first-class interoperability signals, reflecting the multilingual scope of PressMint and ParlaMint corpora projects.

We present a new European Portuguese corpus of newspapers from the 19th and early 20th centuries, integrated in the recent PressMint project, whose goal is to provide a set of comparable newspaper corpora for European languages in that time frame. We discuss the raw data that was previously available, as well as new data specifically compiled for the project, and the challenges involving OCR, text recognition, and different orthographical norms. We describe the pipeline setup for XML encoding and annotation, partially based on work developed for the ParlaMint corpora. The corpus is currently under development and will be made freely available at the end of the project, as part of the PressMint corpora.

This paper presents the historical newspaper collection of the General Regionally Annotated Corpus of Ukrainian (GRAC) and outlines its prospective integration into the PressMint infrastructure. The collection comprises 117 newspaper titles published before 1950, totaling 23.6 million tokens, and reflects the political fragmentation, regional variation, and orthographic diversity of Ukrainian-language press from the late nineteenth to mid-twentieth century. We describe the corpus composition, temporal and geographic distribution, and metadata architecture. Special attention is given to morphosyntactic annotation challenges arising from the old Western Ukrainian orthography (Zhelekhivka), as well as issues related to annotating historical texts using the rule-based TagText parser and neural UDPipe2 models. The paper compares GRAC’s vertical format and metadata system with the TEI-based PressMint standard, identifying technical and conceptual harmonization challenges. Integrating GRAC newspapers into PressMint will facilitate comparative research on language policy, regional standardization, and media discourse within a broader European context.

This paper describes CLARIAH-ES’s contribution to PressMint in Spain as a distributed effort across regional nodes (e.g., Catalonia, Madrid, Basque Country, Galicia, Canary Islands, Alicante), each developing manageable corpora in partnership with key repositories such as ARCA, Patrimonio Digital Complutense, Euskariana, Jable, Galiciana, and the BVMC periodicals portal. A central technical challenge is heterogeneous legacy OCR quality, motivating experiments with AI/LLM-assisted OCR renewal, normalization layers, and linguistic enrichment (e.g., NER and entity linking). This effort is situated alongside ongoing dissemination and the EOSC Mesh “historical newspapers” use-case work aimed at scalable discovery, access, and federated computation over interoperable historical press data.

In this paper the PressMint-AT project is presented, which aims to create a historical newspaper corpus based on the Wiener Abendpost. The quality of automatic text recognition (ATR) is a key factor in creating historical newspaper corpora. Therefore, the performance of established ATR tools, multimodal large language models (LLMS), and existing full-text transcriptions provided by the Austrian National Library via ANNO is evaluated in order identify the most suitable approach for the PressMint-AT project. Even though recent research has demonstrated promising results for OCR tasks using multimodal LLMs, the experiments presented in this paper show, that PERO OCR achieves the best performance for the PressMint-AT dataset.

Digitized literary corpora of the 19th century largely focus on standalone volumes, sidelining the broader and more diverse literary production of the period. Fiction published in less enduring formats – such as novellas and serialized pieces in newspapers – remains underexplored, particularly for low-resource languages like Danish, despite the growing availability of digitized newspaper archives. This paper addresses that gap by identifying and tagging fiction in Danish newspapers (1666–1850). We (1) present a manually annotated dataset of 1,831 articles with both binary (fiction/nonfiction) and fine-grained subcategories (travelogue, biography, essay), and (2) evaluate a document-embedding classifier that achieves an F1-score of up to 0.89 for the fiction/nonfiction distinction. Building on this pipeline, we further provide two resources for future research: (a) fiction probability scores for nearly five million newspaper articles (n=4,898,084), and (b) a small, cleaned, and curated subset of newspaper fiction (n=139), intended as a growing resource.

PressMint is a CLARIN initiative that aims to build multilingual, comparable and interoperable corpora of historical newspapers. For Hungarian, the main challenge is not a lack of material but fragmentation: newspapers are distributed across several portals, with heterogeneous metadata, access paths and OCR quality. This extended abstract reports the current status of the Hungarian PressMint subcorpus, focusing on the 19th century and the early 20th century (roughly 1800–1920). We describe two project artefacts already used in practice: a structured source inventory and a validation-driven repository. We summarise source scouting across Europeana, Hungaricana, OSZK–EPA, DiFMOE and related portals, including a curated 12-title Hungaricana manual-download pilot list with explicit target coverage periods. We then outline a reproducible pipeline for acquisition, OCR, layout analysis and conversion to PressMint-compatible TEI with facsimile linkage. Finally, we specify near-term deliverables for a first Hungarian release candidate and the evaluation steps planned for OCR and layout processing.

The determine the reading order of the text extracted from a searchable PDF produced by an OCR software from an old newspaper is the first task in the process of preparation of corpora of old newspapers. In the paper we present an algorithm for generation of reading order of black selected from the corresponding PDF. Also we performed a tuning of the parameters of the algorithm. The optimization provides 10 % improvement.

This paper presents a survey of the preservation and digitisation status of the German-language press published in interwar Lithuania which existed between 1918 and 1940. Produced within this newly established and ethnically diverse republic which was operating in accordance with the European Minority Protection Regime, German-language newspapers and other periodicals formed a relevant part of the country’s multilingual press. They represent an interesting yet underexplored resource for historical and linguistic research. The survey summarises bibliographic information and the results of earlier digitisation projects. It further addresses challenges for optical character recognition (OCR) of newspaper facsimiles. Although systematic digitisation remains future work, the paper identifies major challenges for OCR within this collection in particular in relation to typographic variation.

The value of digitized historical media archives for computational historical research is now well established, yet an underexplored challenge concerns data management itself: how to represent and process, at scale, complex primary sources that vary widely in digitization granularity, refinement quality, and archival organization and curation practices. This paper presents the data representation framework designed for large-scale processing and indexing of historical newspapers and radio broadcasts developed within the Impresso project. Grounded in a structured characterization of the heterogeneity found in digitized historical media collections, it identifies the distinct dimensions along which collections diverge and the challenges they pose for a unified representation and processing. The framework navigates the competing demands of machine learning pipelines requiring uniform and lightweight document representations, information retrieval systems requiring well-defined indexable content units, user-facing interfaces requiring fidelity to original sources, and the need to return semantically enriched data to archival holders in interoperable formats. We describe the design principles guiding the framework and discuss how it reconciles these constraints across highly heterogeneous collections into a unified and research-ready corpus.

The digitization of Spanish historical newspapers poses significant challenges due to low scan quality, typographical diversity, complex layouts and linguistic variation from contemporary Spanish. While advances in Optical Character Recognition (OCR) and layout-aware models offer promising results, their effectiveness strongly depends on the quality and consistency of the underlying training corpora. This work focuses on corpus construction and evaluation for historical document processing. Two experiments were conducted. In the first corpus los101 was used, a manually curated and structurally annotated subcorpus derived from historical Spanish newspapers, designed to ensure coherent ground truth under heterogeneous real-world conditions. This corpus enables systematic experimentation across OCR and document layout analysis tasks. In a second experimental phase, we apply an additional layout-focused corpus characterized by structural regularity and consistent page organization, allowing us to isolate the impact of layout homogeneity on segmentation performance. State-of-the-art OCR models and a layout detection model are evaluated as validation instruments to assess corpus adequacy rather than as primary contributions. Quantitative and qualitative analyses based on (1) relationship between annotation quality, (2) structural variability, and (3) model behavior, show that heterogeneous corpora challenge both transcription and segmentation stability, while layout-consistent data significantly improves structural detection reliability.

This paper explores the thematic landscapes of three Slovene historical periodicals—Slovenka, Slovenec, and Slovenski narod—from the sPeriodika corpus, a comprehensive collection of Slovene press published between 1771 and 1914. Using BERTopic, we analyse the thematic profiles of these periodicals, enriched with diachronic perspectives. Our study examines the thematic commonalities and specificities of the selected periodicals, highlighting their distinct political orientations, target audiences, and the increasing nationalist polarisation in public discourse. This work contributes to digital humanities by demonstrating the potential of modern topic modelling techniques, such as BERTopic, to advance historical and cultural research.