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Recognizing challenging handwritten annotations with fully convolutional networks
MindGarage, University of Kaiserslautern, Germany; Insiders Technologies GmbH, Kaiserslautern, Germany.
MindGarage, University of Kaiserslautern, Germany.
MindGarage, University of Kaiserslautern, Germany; Insiders Technologies GmbH, Kaiserslautern, Germany.
MindGarage, University of Kaiserslautern, Germany; Insiders Technologies GmbH, Kaiserslautern, Germany.
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2018 (English)In: Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR 2018, IEEE, 2018, p. 25-31, article id 8563221Conference paper, Published paper (Refereed)
Abstract [en]

This paper introduces a very challenging dataset of historic German documents and evaluates Fully Convolutional Neural Network (FCNN) based methods to locate handwritten annotations of any kind in these documents. The handwritten annotations can appear in form of underlines and text by using various writing instruments, e.g., the use of pencils makes the data more challenging. We train and evaluate various end-to-end semantic segmentation approaches and report the results. The task is to classify the pixels of documents into two classes: Background and handwritten annotation. The best model achieves a mean Intersection over Union (IOU) score of 95.6% on the test documents of the presented dataset. We also present a comparison of different strategies used for data augmentation and training on our presented dataset. For evaluation, we use the Layout Analysis Evaluator for the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts.

Place, publisher, year, edition, pages
IEEE, 2018. p. 25-31, article id 8563221
Series
International Conference on Handwriting Recognition, ISSN 2167-6445
Keywords [en]
Annotation Detection, Deep Learning, Segmentation
National Category
Computer Sciences
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-72980DOI: 10.1109/ICFHR-2018.2018.00014ISI: 000454983200005Scopus ID: 2-s2.0-85060050050ISBN: 978-1-5386-5875-8 (print)OAI: oai:DiVA.org:ltu-72980DiVA, id: diva2:1290879
Conference
16th International Conference on Frontiers in Handwriting Recognition( ICFHR 2018), 5-8 August 2018, Niagara Falls, United States
Available from: 2019-02-21 Created: 2019-02-21 Last updated: 2019-03-11Bibliographically approved

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Liwicki, Marcus

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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More styles
Language
  • de-DE
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  • nn-NB
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More languages
Output format
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  • asciidoc
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