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Labeling, Cutting, ...
Labeling, Cutting, Grouping : An Efficient Text Line Segmentation Method for Medieval Manuscripts
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- Alberti, Michele (författare)
- Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland
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- Vögtlin, Lars (författare)
- Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland
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- Pondenkandath, Vinaychandran (författare)
- Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland
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- Seuret, Mathias (författare)
- Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland. Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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- Ingold, Rolf (författare)
- Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland
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- Liwicki, Marcus (författare)
- Luleå tekniska universitet,EISLAB,Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland
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Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany (creator_code:org_t)
- IEEE, 2019
- 2019
- Engelska.
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Ingår i: The 15th IAPR International Conference on Document Analysis and Recognition. - : IEEE. ; , s. 1200-1206
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- textline segmentation
- neural networks
- document image analysis
- Maskininlärning
- Machine Learning
Publikations- och innehållstyp
- vet (ämneskategori)
- kon (ämneskategori)