Named Entity Recognition from Unstructured Handwritten Document Images

Adak, C; Chaudhuri, BB; Blumenstein, M

Named Entity Recognition from Unstructured Handwritten Document Images

Adak, C

Chaudhuri, BB Blumenstein, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 12th IAPR International Workshop on Document Analysis Systems, DAS 2016, 2016, pp. 375 - 380
Issue Date:: 2016-06-10

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Field	Value	Language
dc.contributor.author	Adak, C https://orcid.org/0000-0002-9085-2770	en_US
dc.contributor.author	Chaudhuri, BB	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. 375 - 380	en_US
dc.identifier.isbn	9781509017928	en_US
dc.identifier.uri	http://hdl.handle.net/10453/87541
dc.description.abstract	© 2016 IEEE. Named entity recognition is an important topic in the field of natural language processing, whereas in document image processing, such recognition is quite challenging without employing any linguistic knowledge. In this paper we propose an approach to detect named entities (NEs) directly from offline handwritten unstructured document images without explicit character/word recognition, and with very little aid from natural language and script rules. At the preprocessing stage, the document image is binarized, and then the text is segmented into words. The slant/skew/baseline corrections of the words are also performed. After preprocessing, the words are sent for NE recognition. We analyze the structural and positional characteristics of NEs and extract some relevant features from the word image. Then the BLSTM neural network is used for NE recognition. Our system also contains a post-processing stage to reduce the true NE rejection rate. The proposed approach produces encouraging results on both historical and modern document images, including those from an Australian archive, which are reported here for the very first time.	en_US
dc.relation.ispartof	Proceedings - 12th IAPR International Workshop on Document Analysis Systems, DAS 2016	en_US
dc.relation.isbasedon	10.1109/DAS.2016.15	en_US
dc.title	Named Entity Recognition from Unstructured Handwritten Document Images	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/Faculty of Engineering and Information Technology/School of Computer Science
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. Named entity recognition is an important topic in the field of natural language processing, whereas in document image processing, such recognition is quite challenging without employing any linguistic knowledge. In this paper we propose an approach to detect named entities (NEs) directly from offline handwritten unstructured document images without explicit character/word recognition, and with very little aid from natural language and script rules. At the preprocessing stage, the document image is binarized, and then the text is segmented into words. The slant/skew/baseline corrections of the words are also performed. After preprocessing, the words are sent for NE recognition. We analyze the structural and positional characteristics of NEs and extract some relevant features from the word image. Then the BLSTM neural network is used for NE recognition. Our system also contains a post-processing stage to reduce the true NE rejection rate. The proposed approach produces encouraging results on both historical and modern document images, including those from an Australian archive, which are reported here for the very first time.

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http://hdl.handle.net/10453/87541