OCR Error Correction Using Statistical Machine Translation

In this paper, we explore the use of a statistical machine translation system for optical character recognition (OCR) error correction. We investigate the use of word and character-level models to support a translation from OCR system output to correct french text. Our experiments show that character and word based machine translation correction make significant improvements to the quality of the text produced through digitization. We test the approach on historical data provided by the National Library of France. It shows a relative Word Error Rate reduction of 60% at the word-level, and 54% at the character level.