Optimizing the Training of Models for Automated Post-Correction of Arbitrary OCR-ed Historical Texts

Tobias Englmeier; Florian Fink; Uwe Springmann; Klaus U. Schulz

doi:10.21248/jlcl.35.2022.232

Optimizing the Training of Models for Automated Post-Correction of Arbitrary OCR-ed Historical Texts

Tobias Englmeier, Florian Fink, Uwe Springmann, Klaus U. Schulz

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Anthology ID:: 2022.jlcl-1.1
Volume:: Journal for Language Technology and Computational Linguistics, Vol. 35 No. 1
Month:: Dec.
Year:: 2022
Address:: Germany
Editor:: Christian Wartena
Venue:: JLCL
SIG:
Publisher:: German Society for Computational Lingustics and Language Technology
Note:
Pages:: 1–27
Language:
URL:: https://aclanthology.org/2022.jlcl-1.1/
DOI:: 10.21248/jlcl.35.2022.232
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Cite (ACL):: Tobias Englmeier, Florian Fink, Uwe Springmann, and Klaus U. Schulz. 2022. Optimizing the Training of Models for Automated Post-Correction of Arbitrary OCR-ed Historical Texts. Journal for Language Technology and Computational Linguistics, 35(1):1–27.
Cite (Informal):: Optimizing the Training of Models for Automated Post-Correction of Arbitrary OCR-ed Historical Texts (Englmeier et al., JLCL 2022)
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PDF:: https://aclanthology.org/2022.jlcl-1.1.pdf

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@article{englmeier-etal-2022-optimizing,
    title = "Optimizing the Training of Models for Automated Post-Correction of Arbitrary {OCR}-ed Historical Texts",
    author = "Englmeier, Tobias  and
      Fink, Florian  and
      Springmann, Uwe  and
      Schulz, Klaus U.",
    editor = "Wartena, Christian",
    journal = "Journal for Language Technology and Computational Linguistics",
    volume = "35",
    number = "1",
    month = "dec.",
    year = "2022",
    address = "Germany",
    publisher = "German Society for Computational Lingustics and Language Technology",
    url = "https://aclanthology.org/2022.jlcl-1.1/",
    doi = "10.21248/jlcl.35.2022.232",
    pages = "1--27"
}

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%0 Journal Article
%T Optimizing the Training of Models for Automated Post-Correction of Arbitrary OCR-ed Historical Texts
%A Englmeier, Tobias
%A Fink, Florian
%A Springmann, Uwe
%A Schulz, Klaus U.
%J Journal for Language Technology and Computational Linguistics
%D 2022
%8 December
%V 35
%N 1
%I German Society for Computational Lingustics and Language Technology
%C Germany
%F englmeier-etal-2022-optimizing
%R 10.21248/jlcl.35.2022.232
%U https://aclanthology.org/2022.jlcl-1.1/
%U https://doi.org/10.21248/jlcl.35.2022.232
%P 1-27

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Markdown (Informal)

[Optimizing the Training of Models for Automated Post-Correction of Arbitrary OCR-ed Historical Texts](https://aclanthology.org/2022.jlcl-1.1/) (Englmeier et al., JLCL 2022)

Optimizing the Training of Models for Automated Post-Correction of Arbitrary OCR-ed Historical Texts (Englmeier et al., JLCL 2022)

ACL

Tobias Englmeier, Florian Fink, Uwe Springmann, and Klaus U. Schulz. 2022. Optimizing the Training of Models for Automated Post-Correction of Arbitrary OCR-ed Historical Texts. Journal for Language Technology and Computational Linguistics, 35(1):1–27.