Conference article

Improving Optical Character Recognition of Finnish Historical Newspapers with a Combination of Fraktur & Antiqua Models and Image Preprocessing

Mika Koistinen
National Library of Finland, The Centre for Preservation and Digitisation, Finland

Kimmo Kettunen
National Library of Finland, The Centre for Preservation and Digitisation, Finland

Tuula Pääkkönen
National Library of Finland, The Centre for Preservation and Digitisation, Finland

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Published in: Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden

Linköping Electronic Conference Proceedings 131:38, p. 277-283

NEALT Proceedings Series 29:38, p. 277-283

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Published: 2017-05-08

ISBN: 978-91-7685-601-7

ISSN: 1650-3686 (print), 1650-3740 (online)


In this paper we describe a method for improving the optical character recognition (OCR) toolkit Tesseract for Finnish historical documents. First we create a model for Finnish Fraktur fonts. Second we test Tesseract with the created Fraktur model and Antiqua model on single images and combinations of images with different image preprocessing methods. Against commercial ABBYY FineReader toolkit our method achieves 27.48% (FineReader 7 or 8) and 9.16% (FineReader 11) improvement on word level. Keywords: Optical Character Recognition, OCR Quality, Digital Image Processing, Binarization, Noise Removal, Tesseract, Finnish, Historical Documents


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