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        <title>A Novel Machine Learning Based Approach for Post-OCR Error Detection</title>
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        <namePart type="given">Shafqat</namePart>
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        <namePart type="family">Sheikh Muhammad</namePart>
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            <namePart type="given">Galia</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T A Novel Machine Learning Based Approach for Post-OCR Error Detection
%A Virk, Shafqat Mumtaz
%A Dannélls, Dana
%A Sheikh Muhammad, Azam
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F virk-etal-2021-novel
%X 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.
%U https://aclanthology.org/2021.ranlp-1.164
%P 1463-1470

Markdown (Informal)

[A Novel Machine Learning Based Approach for Post-OCR Error Detection](https://aclanthology.org/2021.ranlp-1.164) (Virk et al., RANLP 2021)

• A Novel Machine Learning Based Approach for Post-OCR Error Detection (Virk et al., RANLP 2021)

ACL

• Shafqat Mumtaz Virk, Dana Dannélls, and Azam Sheikh Muhammad. 2021. A Novel Machine Learning Based Approach for Post-OCR Error Detection. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1463–1470, Held Online. INCOMA Ltd..