Preserving Text Content from Historical Handwritten Documents

Chakraborty, A; Blumenstein, M

Preserving Text Content from Historical Handwritten Documents

Chakraborty, A Blumenstein, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 12th IAPR International Workshop on Document Analysis Systems, DAS 2016, 2016, pp. 329 - 334
Issue Date:: 2016-06-10

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Field	Value	Language
dc.contributor.author	Chakraborty, A	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.date.issued	2016-06-10	en_US
dc.identifier.citation	Proceedings - 12th IAPR International Workshop on Document Analysis Systems, DAS 2016, 2016, pp. 329 - 334	en_US
dc.identifier.isbn	9781509017928	en_US
dc.identifier.uri	http://hdl.handle.net/10453/92714
dc.description.abstract	© 2016 IEEE. We propose a holistic, dynamic method to preserve text content with zero tolerance while removing marginal noise for historical handwritten document images. The key idea is to identify and analyze the region between the sharp peak at the edge and page frame of the text content at each margin. Depending on the proximity of the sharp peak to the text, the text content is then extracted from the document image. This method automatically adapts thresholds for each single document image and is directly applicable to gray-scale images. The proposed method is evaluated on four diverse handwritten historical datasets: Queensland State Archive (QSA), Saint Gall, Parzival and the Prosecution Project. Experimental results show that the proposed method achieves higher accuracy compared with other methods tested on the Saint Gall and Parzival datasets, whilst for the other two Australian datasets, which have been introduced here for the first time, the results are very encouraging.	en_US
dc.relation.ispartof	Proceedings - 12th IAPR International Workshop on Document Analysis Systems, DAS 2016	en_US
dc.relation.isbasedon	10.1109/DAS.2016.77	en_US
dc.title	Preserving Text Content from Historical Handwritten Documents	en_US
dc.type	Conference Proceeding
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. We propose a holistic, dynamic method to preserve text content with zero tolerance while removing marginal noise for historical handwritten document images. The key idea is to identify and analyze the region between the sharp peak at the edge and page frame of the text content at each margin. Depending on the proximity of the sharp peak to the text, the text content is then extracted from the document image. This method automatically adapts thresholds for each single document image and is directly applicable to gray-scale images. The proposed method is evaluated on four diverse handwritten historical datasets: Queensland State Archive (QSA), Saint Gall, Parzival and the Prosecution Project. Experimental results show that the proposed method achieves higher accuracy compared with other methods tested on the Saint Gall and Parzival datasets, whilst for the other two Australian datasets, which have been introduced here for the first time, the results are very encouraging.

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http://hdl.handle.net/10453/92714