DocumentCode :
2489750
Title :
Incremental classification of invoice documents
Author :
Hamza, Hatem ; Belaïd, Yolande ; Belaïd, Abdel ; Chaudhuri, Bidyut B.
Author_Institution :
ITESOFT
fYear :
2008
fDate :
8-11 Dec. 2008
Firstpage :
1
Lastpage :
4
Abstract :
This paper deals with incremental classification and its particular application to invoice classification. An improved version of an already existant incremental neural network called IGNG (incremental growing neural gas) is used for this purpose. This neural network tries to cover the space of data by adding or deleting neurons as data is fed to the system. The improved version of the IGNG, called I2GNG used local thresholds in order to create or delete neurons. Applied on invoice documents represented with graphs, I2GNG shows a recognition rate of 97.63%.
Keywords :
document handling; learning (artificial intelligence); neural nets; pattern classification; incremental classification; incremental growing neural gas; incremental neural network; invoice documents; Databases; Equations; Image segmentation; Machine learning; Neural networks; Neurons; Self organizing feature maps; Tree graphs;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location :
Tampa, FL
ISSN :
1051-4651
Print_ISBN :
978-1-4244-2174-9
Electronic_ISBN :
1051-4651
Type :
conf
DOI :
10.1109/ICPR.2008.4761832
Filename :
4761832
Link To Document :
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