A Novel Machine Learning Based Approach for Post-OCR Error Detection
Shafqat Mumtaz Virk, Dana Dannélls · 2021
Post processing is the most conventional approach for correcting errors that are caused by Optical Character Recognition (OCR) systems.Two steps are usually taken to correct OCR errors: detection and corrections.For the first task, supervised machine learning methods have shown state-of-the-art performances.Previously proposed approaches have focused most prominently on combining lexical, contextual and statistical features for detecting errors.In this study, we report a novel system to error detection which is based merely on the n-gram counts of a candidate token.In addition to being simple and computationally less expensive, our proposed system beats previous systems reported in the ICDAR2019 competition on OCR-error detection with notable margins.We achieved state-of-the-art F1-scores for eight out of the ten involved European languages.The maximum improvement is for Spanish which improved from 0.69 to 0.90, and the minimum for Polish from 0.82 to 0.84.