  Our study utilizes deep learning methods for the automated transcription of
late nineteenth- and early twentieth-century periodicals written in Arabic
script Ottoman Turkish (OT) using the Transkribus platform. We discuss the
historical situation of OT text collections and how they were excluded for the
most part from the late twentieth century corpora digitization that took place
in many Latin script languages. This exclusion has two basic reasons: the
technical challenges of OCR for Arabic script languages, and the rapid
abandonment of that very script in the Turkish historical context. In the
specific case of OT, opening periodical collections to digital tools require
training HTR models to generate transcriptions in the Latin writing system of
contemporary readers of Turkish, and not, as some may expect, in right-to-left
Arabic script text. In the paper we discuss the challenges of training such
models where one-to-one correspondence between the writing systems do not
exist, and we report results based on our HTR experiments with two OT
periodicals from the early twentieth century. Finally, we reflect on potential
domain bias of HTR models in historical languages exhibiting spatio-temporal
variance as well as the significance of working between writing systems for
language communities that have experienced language reform and script change.
