Publication Details

AT-ST: Self-Training Adaptation Strategy for OCR in Domains with Limited Transcriptions

KIŠŠ Martin, BENEŠ Karel and HRADIŠ Michal. AT-ST: Self-Training Adaptation Strategy for OCR in Domains with Limited Transcriptions. In: Lladós J., Lopresti D., Uchida S. (eds) Document Analysis and Recognition - ICDAR 2021. Lecture Notes in Computer Science, vol. 12824. Lausanne: Springer Nature Switzerland AG, 2021, pp. 463-477. ISBN 978-3-030-86336-4. Available from: https://pero.fit.vutbr.cz/publications
Czech title
AT-ST: Samoučící strategie adaptace pro OCR v doménách s omezeným počtem přepisů
Type
conference paper
Language
english
Authors
Kišš Martin, Ing. (DCGM FIT BUT)
Beneš Karel, Ing. (DCGM FIT BUT)
Hradiš Michal, Ing., Ph.D. (DCGM FIT BUT)
URL
Keywords

self-training, text recognition, language model, unlabelled data, confidence measures, data augmentation.

Published
2021
Pages
463-477
Proceedings
Lladós J., Lopresti D., Uchida S. (eds) Document Analysis and Recognition - ICDAR 2021
Series
Lecture Notes in Computer Science
Volume
12824
Conference
International Conference on Document Analysis and Recognition, Lausanne, Switzerland, CH
ISBN
978-3-030-86336-4
Publisher
Springer Nature Switzerland AG
Place
Lausanne, CH
DOI
10.1007/978-3-030-86337-1_31
UT WoS
000711880100031
EID Scopus
2-s2.0-85115292729
BibTeX 
@INPROCEEDINGS{FITPUB12464,
   author = "Martin Ki\v{s}\v{s} and Karel Bene\v{s} and Michal Hradi\v{s}",
   title = "AT-ST: Self-Training Adaptation Strategy for OCR in Domains with Limited Transcriptions",
   pages = "463--477",
   booktitle = "Llad\'{o}s J., Lopresti D., Uchida S. (eds) Document Analysis and Recognition - ICDAR 2021",
   series = "Lecture Notes in Computer Science",
   volume = 12824,
   year = 2021,
   location = "Lausanne, CH",
   publisher = "Springer Nature Switzerland AG",
   ISBN = "978-3-030-86336-4",
   doi = "10.1007/978-3-030-86337-1\_31",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/12464"
}
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