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How to Segment Handwritten Historical Chronicles Using Fully Convolutional Networks?

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F22%3A43965692" target="_blank" >RIV/49777513:23520/22:43965692 - isvavai.cz</a>

  • Result on the web

    <a href="https://link.springer.com/chapter/10.1007/978-3-031-10161-8_9" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-10161-8_9</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-031-10161-8_9" target="_blank" >10.1007/978-3-031-10161-8_9</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    How to Segment Handwritten Historical Chronicles Using Fully Convolutional Networks?

  • Original language description

    This paper deals with historical document image segmentation with focus on chronicles available in the Porta fontium portal. We build on our previously published database that has precise pixel-level annotations in PAGE format but also utilise other datasets for transfer learning in order to improve the results. We discuss a series of experiments that evaluate possibilities how to train a neural model for image, text and background segmentation. The outcome, in a form of segmentation method with relatively low computational costs and great results, is integrated into the Porta fontium portal to improve its possibilities of searching and publication of the documents.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Agents and Artificial Intelligence : Lecture Notes in Computer Science

  • ISBN

    978-3-031-10160-1

  • ISSN

    0302-9743

  • e-ISSN

    1611-3349

  • Number of pages

    16

  • Pages from-to

    181-196

  • Publisher name

    Springer

  • Place of publication

    Cham

  • Event location

    Virtual Event

  • Event date

    Feb 4, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000876376200009