A new framework for recognition of heavily degraded characters in historical typewritten documents based on semi-supervised clustering

Pletschacher, S, Hu, J and Antonacopoulos, A ORCID: https://orcid.org/0000-0001-9552-0233 2009, A new framework for recognition of heavily degraded characters in historical typewritten documents based on semi-supervised clustering , in: 10th International Conference on Document Analysis and Recognition, 26th - 29th July 2009, Barcelona.

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Abstract

This paper presents a new semi-supervised clustering framework to the recognition of heavily degraded characters in historical typewritten documents, where off-theshelf OCR typically fails. The constraints are generated using typographical (collection-independent) domain knowledge and are used to guide both sample (glyph set) partitioning and metric learning. Experimental results using simple features provide encouraging evidence that this approach can lead to significantly improved clustering results compared to simple K-Means clustering, as well as to clustering using a state-of-the art OCR engine.

Item Type:	Conference or Workshop Item (Paper)
Themes:	Subjects outside of the University Themes
Schools:	Schools > School of Health and Society > Centre for Applied Research in Health, Welfare and Policy
Publisher:	IEEE
Refereed:	Yes
Depositing User:	Users 29196 not found.
Date Deposited:	21 Dec 2011 11:37
Last Modified:	27 Mar 2019 12:01
URI:	http://usir.salford.ac.uk/id/eprint/19258

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