A holistic classification system for check amounts based on neural networks with rejection

Authors:
M. J. Castro;W. Díaz;F. J. Ferri;J. Ruiz-Pinales;R. Jaime-Rivas;F. Blat;S. España;P. Aibar;S. Grau;D. Griol
Affiliations:
Dep. Sistemas Informáticos y Computación, Univ. Politécnica de Valencia, Spain;Dep. Informática, Univ. de València, Burjassot, Valencia, Spain;Dep. Informática, Univ. de València, Burjassot, Valencia, Spain;FIMEE – Univ. de Guanajuato, Salamanca, Guanajuato, Mexico;FIMEE – Univ. de Guanajuato, Salamanca, Guanajuato, Mexico;Dep. Sistemas Informáticos y Computación, Univ. Politécnica de Valencia, Spain;Dep. Sistemas Informáticos y Computación, Univ. Politécnica de Valencia, Spain;Dep. Lenguajes y Sistemas Informáticos, Univ. Jaume I, Castellón, Spain;Dep. Sistemas Informáticos y Computación, Univ. Politécnica de Valencia, Spain;Dep. Sistemas Informáticos y Computación, Univ. Politécnica de Valencia, Spain
Venue:
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Year:
2005

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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.