Unsupervised code-switching for multilingual historical document transcription

Abstract

Transcribing documents from the printing press era, a challenge in its own right, is more complicated when documents interleave multiple languages - a common feature of 16th century texts. Additionally, many of these documents precede consistent orthographic conventions, making the task even harder. We extend the state-of-the-art historical OCR model of Berg-Kirkpatrick et al. (2013) to handle word-level code-switching between multiple languages. Further, we enable our system to handle spelling variability, including now-obsolete shorthand systems used by printers. Our results show average relative character error reductions of 14% across a variety of historical texts.