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

Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

Volumn , Issue , 2009, Pages 506-510

  Pletschacher, S ^a   Hu, J ^b   Antonacopoulos, A ^a  

^a UNIVERSITY OF SALFORD   (United Kingdom)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING RESULTS; DOMAIN KNOWLEDGE; K-MEANS CLUSTERING; METRIC LEARNING; OCR ENGINES; SEMI-SUPERVISED CLUSTERING; STATE OF THE ART;

CLUSTERING ALGORITHMS;

EID: 71249087609     PISSN: 15205363     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDAR.2009.267     Document Type: Conference Paper

References (11)

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2. A. Antonacopoulos and D. Karatzas, "Semantics-Based Content Extraction in Typewritten Historical Documents", Proc. 8th Int. Conf. on Document Analysis and Recognition (ICDAR2005), Seoul, South Korea, 2005, pp. 48-53.
3. A. Antonacopoulos and C. Casado Castilla, "Flexible Text Recovery from Degraded Typewritten Historical Documents", Proc. 18th Int. Conf. on Pattern Recognition (ICPR2006), Hong Kong, 2006, pp. 1062-1065.
4. th Int. Conf. on Document Analysis and Recognition (ICDAR2003), Edinburgh, 2003, pp. 462-466.
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10. K. Wagstaff, C. Cardie, S. Rogers and S. Schroedl, "Constrained K-means Clustering with Background Knowledge", Proc. 18th ICML, Massachusetts, US, 2001.
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