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

TL;DR

This paper explores deep learning-based methods for automatically transcribing Ottoman Turkish periodicals written in Arabic script into Latin script, addressing historical, technical, and linguistic challenges.

Contribution

It introduces a novel approach to train HTR models for Ottoman Turkish, converting Arabic script texts into Latin script, and discusses the implications of script change and domain bias.

Findings

Successful training of HTR models for Ottoman Turkish periodicals

Demonstrated transcriptions in Latin script from Arabic script texts

Highlighted challenges of script conversion and domain bias

Abstract

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.