Layout analysis and content enrichment of digitized books.

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Title: Layout analysis and content enrichment of digitized books.
Authors: Grana, Costantino1 costantino.grana@unimore.it, Serra, Giuseppe1 giuseppe.serra@unimore.it, Manfredi, Marco1 marco.manfredi@unimore.it, Coppi, Dalia1 dalia.coppi@unimore.it, Cucchiara, Rita1 rita.cucchiara@unimore.it
Source: Multimedia Tools & Applications. Apr2016, Vol. 75 Issue 7, p3879-3900. 22p.
Subjects: Electronic books, Layout (Printing), Digitization, Supervised learning, Hyperlinks
Abstract: In this paper we describe a system for automatically analyzing old documents and creating hyper linking between different epochs, thus opening ancient documents to young people and to make them available on the web with old and current content. We propose a supervised learning approach to segment text and illustration of digitized old documents using a texture feature based on local correlation aimed at detecting the repeating patterns of text regions and differentiate them from pictorial elements. Moreover we present a solution to help the user in finding contemporary content connected to what is automatically extracted from the ancient documents. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="AR" term="%22Grana%2C+Costantino%22">Grana, Costantino</searchLink><relatesTo>1</relatesTo><i> costantino.grana@unimore.it</i><br /><searchLink fieldCode="AR" term="%22Serra%2C+Giuseppe%22">Serra, Giuseppe</searchLink><relatesTo>1</relatesTo><i> giuseppe.serra@unimore.it</i><br /><searchLink fieldCode="AR" term="%22Manfredi%2C+Marco%22">Manfredi, Marco</searchLink><relatesTo>1</relatesTo><i> marco.manfredi@unimore.it</i><br /><searchLink fieldCode="AR" term="%22Coppi%2C+Dalia%22">Coppi, Dalia</searchLink><relatesTo>1</relatesTo><i> dalia.coppi@unimore.it</i><br /><searchLink fieldCode="AR" term="%22Cucchiara%2C+Rita%22">Cucchiara, Rita</searchLink><relatesTo>1</relatesTo><i> rita.cucchiara@unimore.it</i>
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  Data: <searchLink fieldCode="DE" term="%22Electronic+books%22">Electronic books</searchLink><br /><searchLink fieldCode="DE" term="%22Layout+%28Printing%29%22">Layout (Printing)</searchLink><br /><searchLink fieldCode="DE" term="%22Digitization%22">Digitization</searchLink><br /><searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Hyperlinks%22">Hyperlinks</searchLink>
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  Data: In this paper we describe a system for automatically analyzing old documents and creating hyper linking between different epochs, thus opening ancient documents to young people and to make them available on the web with old and current content. We propose a supervised learning approach to segment text and illustration of digitized old documents using a texture feature based on local correlation aimed at detecting the repeating patterns of text regions and differentiate them from pictorial elements. Moreover we present a solution to help the user in finding contemporary content connected to what is automatically extracted from the ancient documents. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11042-014-2360-0
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        Text: English
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      – SubjectFull: Digitization
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              Text: Apr2016
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