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A Holistic Classification System for Check Amounts Based on Neural Networks with Rejection.
- Source :
- Pattern Recognition & Machine Intelligence; 2005, p310-314, 5p
- Publication Year :
- 2005
-
Abstract
- A holistic classification system for off-line recognition of legal amounts in checks is described in this paper. The binary images obtained from the cursive words are processed following the human visual system, employing a Hough transform method to extract perceptual features. Images are finally coded into a bidimensional feature map representation. Multilayer perpeptrons are used to classify these feature maps into one of the 32 classes belonging to the CENPARMI database. To select a final classification system, ROC graphs are used to fix the best threshold values of the classifiers to obtain the best tradeoff between accuracy and misclassification. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540305064
- Database :
- Complementary Index
- Journal :
- Pattern Recognition & Machine Intelligence
- Publication Type :
- Book
- Accession number :
- 32965660
- Full Text :
- https://doi.org/10.1007/11590316_45