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Our study utilizes deep learning methods for the automated transcription oflate nineteenth- and early twentieth-century periodicals written in Arabicscript Ottoman Turkish (OT) using the Transkribus platform. We discuss thehistorical situation of OT text collections and how they were excluded for themost part from the late twentieth century corpora digitization that took placein many Latin script languages. This exclusion has two basic reasons: thetechnical challenges of OCR for Arabic script languages, and the rapidabandonment of that very script in the Turkish historical context. In thespecific case of OT, opening periodical collections to digital tools requiretraining HTR models to generate transcriptions in the Latin writing system ofcontemporary readers of Turkish, and not, as some may expect, in right-to-leftArabic script text. In the paper we discuss the challenges of training suchmodels where one-to-one correspondence between the writing systems do notexist, and we report results based on our HTR experiments with two OTperiodicals from the early twentieth century. Finally, we reflect on potentialdomain bias of HTR models in historical languages exhibiting spatio-temporalvariance as well as the significance of working between writing systems forlanguage communities that have experienced language reform and script change.

Automated Transcription of Non-Latin Script Periodicals: A Case Study in the Ottoman Turkish Print Archive