Optimizing the Training of Models for Automated Post-Correction of Arbitrary OCR-ed Historical Texts
Tobias Englmeier, Florian Fink, Uwe Springmann, Klaus U. Schulz
- 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://preview.aclanthology.org/moar-dois/2022.jlcl-1.1/
- DOI:
- 10.21248/jlcl.35.2022.232
- 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)
- PDF:
- https://preview.aclanthology.org/moar-dois/2022.jlcl-1.1.pdf