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Labeling, Cutting, ...
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Alberti, MicheleDocument Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland
(författare)
Labeling, Cutting, Grouping : An Efficient Text Line Segmentation Method for Medieval Manuscripts
- Artikel/kapitelEngelska2019
Förlag, utgivningsår, omfång ...
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IEEE,2019
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printrdacarrier
Nummerbeteckningar
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LIBRIS-ID:oai:DiVA.org:ltu-78685
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https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-78685URI
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https://doi.org/10.1109/ICDAR.2019.00194DOI
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Språk:engelska
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Sammanfattning på:engelska
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Ämneskategori:vet swepub-contenttype
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Ämneskategori:kon swepub-publicationtype
Anmärkningar
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ISBN för värdpublikation: 978-1-7281-3014-9, 978-1-7281-3015-6
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This paper introduces a new way for text-line extraction by integrating deep-learning based pre-classification and state-of-the-art segmentation methods. Text-line extraction in complex handwritten documents poses a significant challenge, even to the most modern computer vision algorithms. Historical manuscripts are a particularly hard class of documents as they present several forms of noise, such as degradation, bleed-through, interlinear glosses, and elaborated scripts. In this work, we propose a novel method which uses semantic segmentation at pixel level as intermediate task, followed by a text-line extraction step. We measured the performance of our method on a recent dataset of challenging medieval manuscripts and surpassed state-of-the-art results by reducing the error by 80.7%. Furthermore, we demonstrate the effectiveness of our approach on various other datasets written in different scripts. Hence, our contribution is two-fold. First, we demonstrate that semantic pixel segmentation can be used as strong denoising pre-processing step before performing text line extraction. Second, we introduce a novel, simple and robust algorithm that leverages the high-quality semantic segmentation to achieve a text-line extraction performance of 99.42% line IU on a challenging dataset.
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Biuppslag (personer, institutioner, konferenser, titlar ...)
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Vögtlin, LarsDocument Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland
(författare)
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Pondenkandath, VinaychandranDocument Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland
(författare)
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Seuret, MathiasDocument Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland. Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
(författare)
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Ingold, RolfDocument Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland
(författare)
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Liwicki, MarcusLuleå tekniska universitet,EISLAB,Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland(Swepub:ltu)marliw
(författare)
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Document Image and Voice Analysis Group (DIVA), University of Fribourg, SwitzerlandDocument Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland. Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:The 15th IAPR International Conference on Document Analysis and Recognition: IEEE, s. 1200-1206
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