Statistical Learning for OCR Text Correction
This work addresses OCR error correction for text analysis pipelines, presenting an incremental improvement over existing models.
The paper tackled the problem of improving OCR text correction by enlarging the candidate suggestion space using external corpus and OCR-specific features in a regression approach, resulting in correction of 61.5% of OCR errors with top 1 suggestions and 71.5% with top 3 suggestions against a theoretical upper-bound of 78%.
The accuracy of Optical Character Recognition (OCR) is crucial to the success of subsequent applications used in text analyzing pipeline. Recent models of OCR post-processing significantly improve the quality of OCR-generated text, but are still prone to suggest correction candidates from limited observations while insufficiently accounting for the characteristics of OCR errors. In this paper, we show how to enlarge candidate suggestion space by using external corpus and integrating OCR-specific features in a regression approach to correct OCR-generated errors. The evaluation results show that our model can correct 61.5% of the OCR-errors (considering the top 1 suggestion) and 71.5% of the OCR-errors (considering the top 3 suggestions), for cases where the theoretical correction upper-bound is 78%.