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A New Framework for Recognition of Heavily Degraded Characters in Historical Typewritten Documents Based on Semi-Supervised Clustering
 

Summary: A New Framework for Recognition of Heavily Degraded Characters in Historical
Typewritten Documents Based on Semi-Supervised Clustering
S. Pletschacher1
, J. Hu2
and A. Antonacopoulos1
1
Pattern Recognition and Image Analysis (PRImA) Research Lab
School of Computing, Science and Engineering, University of Salford, Greater Manchester, United Kingdom
http://www.primaresearch.org
2
IBM T.J. Watson Research Center
1101 Kitchawan Road, Route 134 Yorktown Heights, NY 10598
jyhu@us.ibm.com

This work has been supported in part through the EU 7th
Framework Programme grant IMPACT (Ref: 215064).
Abstract
This paper presents a new semi-supervised clustering
framework to the recognition of heavily degraded charac-
ters in historical typewritten documents, where off-the-

  

Source: Antonacopoulos, Apostolos - School of Computing, Science and Engineering, University of Salford

 

Collections: Computer Technologies and Information Sciences