BibTeX

@inProceedings{virk-etal-2021-novel-306962,
	title        = {A Novel Machine Learning Based Approach for Post-OCR Error Detection},
	abstract     = {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. },
	booktitle    = {Proceedings of the International Conference on Recent Advances in Natural Language Processing, 1–3 September, 2021 / Edited by Galia Angelova, Maria Kunilovskaya, Ruslan Mitkov, Ivelina Nikolova-Koleva},
	author       = {Virk, Shafqat and Dannélls, Dana and  Muhammad, Azam Sheikh},
	year         = {2021},
	publisher    = {INCOMA},
	address      = {Shoumen, Bulgaria},
	ISBN         = {978-954-452-072-4},
}