OCR Processing of Swedish Historical Newspapers Using Deep Hybrid CNNLSTM Networks

Molly Brandt Skelbye, Dana Dannélls


Abstract
Deep CNN–LSTM hybrid neural networks have proven to improve the accuracy of Optical Character Recognition (OCR) models for different languages. In this paper we examine to what extent these networks improve the OCR accuracy rates on Swedish historical newspapers. By experimenting with the open source OCR engine Calamari, we are able to show that mixed deep CNN–LSTM hybrid models outperform previous models on the task of character recognition of Swedish historical newspapers spanning 1818–1848. We achieved an average character accuracy rate (CAR) of 97.43% which is a new state–of–the–art result on 19th century Swedish newspaper text. Our data, code and models are released under CC-BY licence.
Anthology ID:
2021.ranlp-1.23
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
190–198
Language:
URL:
https://aclanthology.org/2021.ranlp-1.23
DOI:
Bibkey:
Cite (ACL):
Molly Brandt Skelbye and Dana Dannélls. 2021. OCR Processing of Swedish Historical Newspapers Using Deep Hybrid CNN–LSTM Networks. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 190–198, Held Online. INCOMA Ltd..
Cite (Informal):
OCR Processing of Swedish Historical Newspapers Using Deep Hybrid CNN–LSTM Networks (Brandt Skelbye & Dannélls, RANLP 2021)
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PDF:
https://aclanthology.org/2021.ranlp-1.23.pdf