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Using Multi-level Segmentation Features for Document Image Classification

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Document Analysis Systems (DAS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13237))

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Abstract

Document Image classification is a crucial step in the processing pipeline for many purposes (e.g. indexing, OCR, keyword spotting) and is being applied at early stages. At this point, textual information about the document (OCR) is usually not available and additional features are required in order to achieve higher recognition accuracy. On the other hand, one may have reliable segmentation information (e.g. text block, paragraph, line, word, symbol segmentation results), extracted also at pre-processing stages. In this paper, visual features are fused with segmentation analysis results in a novel integrated workflow and end-to-end training can be easily applied. Significant improvements on popular datasets (Tobacco-3482 and RVL-CDIP) are presented, when compared to state-of-the-art methodologies which consider visual features.

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Notes

  1. 1.

    https://www.piop.gr/en/vivliothiki.aspx.

  2. 2.

    http://culdile.bookscanner.gr.

  3. 3.

    https://github.com/tesseract-ocr/tesseract.

  4. 4.

    https://github.com/facebookresearch/fastText.

  5. 5.

    https://www.tensorflow.org.

  6. 6.

    https://cloud.google.com/vision.

  7. 7.

    https://www.piop.gr/en/vivliothiki.aspx.

  8. 8.

    http://culdile.bookscanner.gr.

  9. 9.

    https://www.snf.org/en/.

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Acknowledgements

This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the RESEARCH-CREATE-INNOVATE call (project code: T1EDK-03785 and acronym: CULDILE) as well as by the program of Industrial Scholarships of Stavros Niarchos FoundationFootnote 9.

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Authors and Affiliations

  1. Computational Intelligence Laboratory, Institute of Informatics and Telecommunications, National Center for Scientific Research Demokritos, 153 10, Agia Paraskevi, Athens, Greece

    Panagiotis Kaddas & Basilis Gatos

  2. Department of Informatics and Telecommunications, University of Athens, 157 84, Athens, Greece

    Panagiotis Kaddas

Authors
  1. Panagiotis Kaddas
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  2. Basilis Gatos
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Corresponding author

Correspondence to Panagiotis Kaddas .

Editor information

Editors and Affiliations

  1. Kyushu University, Fukuoka, Japan

    Seiichi Uchida

  2. Boise State University, BOISE, ID, USA

    Elisa Barney

  3. LIRIS UMR CNRS, Villeurbanne, France

    Véronique Eglin

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Kaddas, P., Gatos, B. (2022). Using Multi-level Segmentation Features for Document Image Classification. In: Uchida, S., Barney, E., Eglin, V. (eds) Document Analysis Systems. DAS 2022. Lecture Notes in Computer Science, vol 13237. Springer, Cham. https://doi.org/10.1007/978-3-031-06555-2_47

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  • DOI: https://doi.org/10.1007/978-3-031-06555-2_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06554-5

  • Online ISBN: 978-3-031-06555-2

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