BART for Post-Correction of OCR Newspaper Text
Elizabeth Soper, Stanley Fujimoto, Yen-Yun Yu · 2021
Optical character recognition (OCR) from newspaper page images is susceptible to noise due to degradation of old documents and variation in typesetting.In this report, we present a novel approach to OCR postcorrection.We cast error correction as a translation task, and fine-tune BART, a transformerbased sequence-to-sequence language model pretrained to denoise corrupted text.We are the first to use sentence-level transformer models for OCR post-correction, and our best model achieves a 29.4% improvement in character accuracy over the original noisy OCR text.Our results demonstrate the utility of pretrained language models for dealing with noisy text.