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Englmeier, Tobias; Fink, Florian; Schulz, Klaus U.

doi:10.1145/3322905.3322908

Proceedings of the 3rd International Conference on Digital Access to Textual Cultural Heritage 2019

DOI: 10.1145/3322905.3322908

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Tobias Englmeier

Florian Fink

Klaus U. Schulz

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Cited by 3 publications

References 16 publications

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Optical character recognition with neural networks and post-correction with finite state methods

Drobac

Lindén

2020

IJDAR

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The optical character recognition (OCR) quality of the historical part of the Finnish newspaper and journal corpus is rather low for reliable search and scientific research on the OCRed data. The estimated character error rate (CER) of the corpus, achieved with commercial software, is between 8 and 13%. There have been earlier attempts to train high-quality OCR models with open-source software, like Ocropy (https://github.com/tmbdev/ocropy) and Tesseract (https://github.com/tesseract-ocr/tesseract), but so far, none of the methods have managed to successfully train a mixed model that recognizes all of the data in the corpus, which would be essential for an efficient re-OCRing of the corpus. The difficulty lies in the fact that the corpus is printed in the two main languages of Finland (Finnish and Swedish) and in two font families (Blackletter and Antiqua). In this paper, we explore the training of a variety of OCR models with deep neural networks (DNN). First, we find an optimal DNN for our data and, with additional training data, successfully train high-quality mixed-language models. Furthermore, we revisit the effect of confidence voting on the OCR results with different model combinations. Finally, we perform post-correction on the new OCR results and perform error analysis. The results show a significant boost in accuracy, resulting in 1.7% CER on the Finnish and 2.7% CER on the Swedish test set. The greatest accomplishment of the study is the successful training of one mixed language model for the entire corpus and finding a voting setup that further improves the results.

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Optical character recognition with neural networks and post-correction with finite state methods

Drobac

Lindén

2020

IJDAR

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Augmented reality material management system based on post-processing of aero-engine blade code recognition

Zhang

Wang

et al. 2022

Journal of Manufacturing Systems

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OCR-D kompakt: Ergebnisse und Stand der Forschung in der Förderinitiative

Baierer

Boenig²,

Engl

et al. 2020

Bibliothek Forschung Und Praxis

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ZusammenfassungBereits seit einigen Jahren werden große Anstrengungen unternommen, um die im deutschen Sprachraum erschienenen Drucke des 16.–18. Jahrhunderts zu erfassen und zu digitalisieren. Deren Volltexttransformation konzeptionell und technisch vorzubereiten, ist das übergeordnete Ziel des DFG-Projekts OCR-D, das sich mit der Weiterentwicklung von Verfahren der Optical Character Recognition befasst. Der Beitrag beschreibt den aktuellen Entwicklungsstand der OCR-D-Software und analysiert deren erste Teststellung in ausgewählten Bibliotheken.

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A-I-PoCoTo

Cited by 3 publications

References 16 publications

Optical character recognition with neural networks and post-correction with finite state methods

Optical character recognition with neural networks and post-correction with finite state methods

Augmented reality material management system based on post-processing of aero-engine blade code recognition

OCR-D kompakt: Ergebnisse und Stand der Forschung in der Förderinitiative

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