Text processing: robust character recognition using calibrated text and diversified feature set

Author(s):  
D.C.D. Hung ◽  
Y. Chen ◽  
R. Chen ◽  
T. Cheng
2001 ◽  
Author(s):  
Robert J. Hines ◽  
Mark A. McDaniel ◽  
Melissa Guynn

Author(s):  
Kjell Ohlsson ◽  
Lars-Goeran Nilsson ◽  
Jerker Roennberg
Keyword(s):  

2007 ◽  
Author(s):  
Matthew Collins ◽  
Betty Ann Levy
Keyword(s):  

1991 ◽  
Author(s):  
Elizabeth Pugzles Lorch ◽  
Robert F. Lorch ◽  
Jonathan M. Golding
Keyword(s):  

1996 ◽  
Vol 35 (03) ◽  
pp. 261-264 ◽  
Author(s):  
T. Schromm ◽  
T. Frankewitsch ◽  
M. Giehl ◽  
F. Keller ◽  
D. Zellner

Abstract:A pharmacokinetic database was constructed that is as free of errors as possible. Pharmacokinetic parameters were derived from the literature using a text-processing system and a database system. A random data sample from each system was compared with the original literature. The estimated error frequencies using statistical methods differed significantly between the two systems. The estimated error frequency in the text-processing system was 7.2%, that in the database system 2.7%. Compared with the original values in the literature, the estimated probability of error for identical pharmacokinetic parameters recorded in both systems is 2.4% and is not significantly different from the error frequency in the database. Parallel data entry with a text-processing system and a database system is, therefore, not significantly better than structured data entry for reducing the error frequency.


1997 ◽  
Vol 9 (1-3) ◽  
pp. 58-77
Author(s):  
Vitaly Kliatskine ◽  
Eugene Shchepin ◽  
Gunnar Thorvaldsen ◽  
Konstantin Zingerman ◽  
Valery Lazarev

In principle, printed source material should be made machine-readable with systems for Optical Character Recognition, rather than being typed once more. Offthe-shelf commercial OCR programs tend, however, to be inadequate for lists with a complex layout. The tax assessment lists that assess most nineteenth century farms in Norway, constitute one example among a series of valuable sources which can only be interpreted successfully with specially designed OCR software. This paper considers the problems involved in the recognition of material with a complex table structure, outlining a new algorithmic model based on ‘linked hierarchies’. Within the scope of this model, a variety of tables and layouts can be described and recognized. The ‘linked hierarchies’ model has been implemented in the ‘CRIPT’ OCR software system, which successfully reads tables with a complex structure from several different historical sources.


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