Robust text line detection in historical documents: learning and evaluation methods

Text line segmentation is one of the key steps in historical document understanding. It is challenging due to the variety of fonts, contents, writing styles and the quality of documents that have degraded through the years. In this paper, we address the limitations that currently prevent people from...

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Published in:	International journal on document analysis and recognition Vol. 25; no. 2; pp. 95 - 114
Main Authors:	Boillet, Mélodie, Kermorvant, Christopher, Paquet, Thierry
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2022 Springer Nature B.V Springer Verlag
Subjects:	Annotations Computer Science Datasets Documents Engineering Sciences Fonts Image Processing and Computer Vision Original Paper Pattern Recognition Segmentation Signal and Image processing Generic architecture Evaluation strategy Text line detection Historical documents Goal-directed evaluation Text line detection ; Historical documents;Generic architecture;Evaluation strategy ; Goal-directed evaluation
ISSN:	1433-2833, 1433-2825
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Abstract	Text line segmentation is one of the key steps in historical document understanding. It is challenging due to the variety of fonts, contents, writing styles and the quality of documents that have degraded through the years. In this paper, we address the limitations that currently prevent people from building line segmentation models with a high generalization capacity. We present a study conducted using three state-of-the-art systems Doc-UFCN, dhSegment and ARU-Net and show that it is possible to build generic models trained on a wide variety of historical document datasets that can correctly segment diverse unseen pages. This paper also highlights the importance of the annotations used during training: Each existing dataset is annotated differently. We present a unification of the annotations and show its positive impact on the final text recognition results. In this end, we present a complete evaluation strategy using standard pixel-level metrics, object-level ones and introducing goal-oriented metrics.
AbstractList	Text line segmentation is one of the key steps in historical document understanding. It is challenging due to the variety of fonts, contents, writing styles and the quality of documents that have degraded through the years. In this paper, we address the limitations that currently prevent people from building line segmentation models with a high generalization capacity. We present a study conducted using three state-of-the-art systems Doc-UFCN, dhSegment and ARU-Net and show that it is possible to build generic models trained on a wide variety of historical document datasets that can correctly segment diverse unseen pages. This paper also highlights the importance of the annotations used during training: Each existing dataset is annotated differently. We present a unification of the annotations and show its positive impact on the final text recognition results. In this end, we present a complete evaluation strategy using standard pixel-level metrics, object-level ones and introducing goal-oriented metrics.
Author	Kermorvant, Christopher Boillet, Mélodie Paquet, Thierry
Author_xml	– sequence: 1 givenname: Mélodie orcidid: 0000-0002-0618-7852 surname: Boillet fullname: Boillet, Mélodie email: boillet@teklia.com organization: TEKLIA, LITIS, Normandie University – sequence: 2 givenname: Christopher surname: Kermorvant fullname: Kermorvant, Christopher organization: TEKLIA, LITIS, Normandie University – sequence: 3 givenname: Thierry surname: Paquet fullname: Paquet, Thierry organization: LITIS, Normandie University
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Keywords	Generic architecture Evaluation strategy Text line detection Historical documents Goal-directed evaluation Text line detection ; Historical documents;Generic architecture;Evaluation strategy ; Goal-directed evaluation
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Snippet	Text line segmentation is one of the key steps in historical document understanding. It is challenging due to the variety of fonts, contents, writing styles...
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Title	Robust text line detection in historical documents: learning and evaluation methods
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