Building a Parallel Corpus on the World's Oldest Banking Magazine

We report on our processing steps to build a diachronic parallel corpus based on the world's oldest banking magazine. The magazine has been published since 1895 in German, with translations in French and partly in English and Italian. Our data sources are printed issues (until 1997), PDF issues (since 1998) and HTML files (since 2001). The corpus building poses special challenges in article boundary recognition and cross-language article and sentence alignment. Our corpus fills a gap in parallel corpora with respect to genre (magazine articles), domain (banking and economy articles), and its time span (120 years)

Historical Newspaper Content Mining: Revisiting the impresso Project's Challenges in Text and Image Processing, Design and Historical Scholarship

impresso. Media Monitoring of the Past is an interdisciplinary research project in which a team of computational linguists, designers and historians collaborate on the datafication of a multilingual corpus of digitised historical newspapers. The primary goals of the project are to improve text mining tools for historical text, to enrich historical newspapers with (semi-) automatically generated data and to integrate such data into historical research workflows by means of a newly developed user interface. In this paper we discuss our efforts to overcome inherent challenges and to integrate text mining and data visualisation applications in general historical research practices which are characterised by search operations as well as the need to create topical collections

Bullingers Briefwechsel zugänglich machen: Stand der Handschriftenerkennung

"Anhand des Briefwechsels Heinrich Bullingers (1504-1575), das rund 10'000 Briefe umfasst, demonstrieren wir den Stand der Forschung in automatisierter Handschriftenerkennung. Es finden sich mehr als hundert unterschiedliche Schreiberhände in den Briefen mit sehr unterschiedlicher Verteilung. Das Korpus ist zweisprachig (Latein/Deutsch) und teilweise findet der Sprachwechsel innerhalb von Abschnitten oder gar Sätzen statt. Auf Grund dieser Vielfalt eignet sich der Briefwechsel optimal als Testumgebung für entsprechende Algorithmen und ist aufschlussreiche für Forschungsprojekte und Erinnerungsinstitutionen mit ähnlichen Problemstellungen. Im Paper werden drei Verfahren gegeneinander gestellt und abgewogen. Im folgenden werde drei Ansätze an dem Korpus getestet, die Aufschlüsse zum Stand und möglichen Entwicklungen im Bereich der Handschriftenerkennung versprechen. Erstens wird mit Transkribus eine etablierte Plattform genutzt, die zwei Engines (HTR+ und PyLaia) anbietet. Zweitens wird mit Hilfe von Data Augmentation versucht die Erkennung mit der state-of-the-art Engine HTRFlor zu verbessern und drittens werden neue Transformer-basierte Modelle (TrOCR) eingesetzt." Ein Beitrag zur 9. Tagung des Verbands "Digital Humanities im deutschsprachigen Raum" - DHd 2023 Open Humanities Open Culture

How Much Data Do You Need? About the Creation of a Ground Truth for Black Letter and the Effectiveness of Neural OCR

Recent advances in Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) have led to more accurate textrecognition of historical documents. The Digital Humanities heavily profit from these developments, but they still struggle whenchoosing from the plethora of OCR systems available on the one hand and when defining workflows for their projects on the other hand.In this work, we present our approach to build a ground truth for a historical German-language newspaper published in black letter. Wealso report how we used it to systematically evaluate the performance of different OCR engines. Additionally, we used this ground truthto make an informed estimate as to how much data is necessary to achieve high-quality OCR results. The outcomes of our experimentsshow that HTR architectures can successfully recognise black letter text and that a ground truth size of 50 newspaper pages suffices toachieve good OCR accuracy. Moreover, our models perform equally well on data they have not seen during training, which means thatadditional manual correction for diverging data is superfluous