Dan Garrette and Hannah Alpert-Abrams. “ An Unsupervised Model of Orthographic Variation for Historical Document Transcription.” Proceedings of NAACL 2016.
Abstract
Historical documents frequently exhibit extensive orthographic variation, including archaic spellings and obsolete shorthand. OCR tools typically seek to produce so-called diplomatic transcriptions that preserve these variants, but many end tasks require transcriptions with normalized orthography. In this paper, we present a novel joint transcription model that learns, unsupervised, a probabilistic mapping between modern orthography and that used in the document. Our system thus produces dual diplomatic and normalized transcriptions simultaneously, and achieves a 35% relative error reduction over a state-of-the-art OCR model on diplomatic transcription, and a 46% reduction on normalized transcription.