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Text Line Segmentation in Historical Newspapers

  • Conference paper
  • First Online:
Artificial Intelligence and Soft Computing (ICAISC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13589))

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Abstract

This paper deals with page segmentation into individual text lines used as an input of a line-based OCR system. This task is usually solved in one step which directly identifies text lines in whole documents. However, a direct approach may jeopardize the reading order of the lines and thus deteriorate the overall transcription result.

We propose a novel approach which decomposes this problem into two steps: text-block and text-line segmentation. The particular tasks are handled by algorithms based on fully convolutional neural networks.

The proposed method is evaluated on two standard corpora, Europeana and RDCL 2019, and on a novel dataset created from data available in Porta fontium portal. This dataset is freely available for research purposes.

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Notes

  1. 1.

    http://www.portafontium.cz/.

  2. 2.

    http://ocr-corpus.kiv.zcu.cz/.

  3. 3.

    http://www.portafontium.eu/.

  4. 4.

    https://scriptnet.iit.demokritos.gr/competitions/11/.

  5. 5.

    https://github.com/Transkribus/TranskribusBaseLineEvaluationScheme.

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Acknowledgements

This work has been partly supported from ERDF “Research and Development of Intelligent Components of Advanced Technologies for the Pilsen Metropolitan Area (InteCom)” (no.: CZ.02.1.01/0.0/0.0/17_048/0007267).

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Plzeň, Czech Republic

    Ladislav Lenc, Jiří Martínek & Pavel Král

  2. NTIS - New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Plzeň, Czech Republic

    Ladislav Lenc, Jiří Martínek & Pavel Král

Authors
  1. Ladislav Lenc
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  2. Jiří Martínek
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  3. Pavel Král
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Corresponding author

Correspondence to Ladislav Lenc .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Lenc, L., Martínek, J., Král, P. (2023). Text Line Segmentation in Historical Newspapers. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13589. Springer, Cham. https://doi.org/10.1007/978-3-031-23480-4_3

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  • DOI: https://doi.org/10.1007/978-3-031-23480-4_3

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  • Online ISBN: 978-3-031-23480-4

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