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19 results on '"Jaime-Rivas, R."'

1. A Holistic Classification System for Check Amounts Based on Neural Networks with Rejection

Author

Castro, M. J., Díaz, W., Ferri, F. J., Ruiz-Pinales, J., Jaime-Rivas, R., Blat, F., España, S., Aibar, P., Grau, S., Griol, D., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Pal, Sankar K., editor, Bandyopadhyay, Sanghamitra, editor, and Biswas, Sambhunath, editor

Published
2005
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4. A Holistic Classification System for Check Amounts Based on Neural Networks with Rejection.

Author

Pal, Sankar K., Bandyopadhyay, Sanghamitra, Biswas, Sambhunath, Castro, M.J., Díaz, W., Ferri, F.J., Ruiz-Pinales, J., Jaime-Rivas, R., Blat, F., España, S., Aibar, P., Grau, S., and Griol, D.

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]

Published
2005
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19. Cursive word recognition based on interactive activation and early visual processing models.

Author

Ruiz-Pinales J, Jaime-Rivas R, Lecolinet E, and Castro-Bleda MJ

Subjects
Animals, Handwriting, Humans, Nerve Net physiology, Pattern Recognition, Visual physiology, Reading, Time Factors, Visual Cortex physiology, Visual Pathways physiology, Algorithms, Artificial Intelligence, Image Processing, Computer-Assisted methods, Neural Networks, Computer, Pattern Recognition, Automated standards
Abstract

We present an off-line cursive word recognition system based completely on neural networks: reading models and models of early visual processing. The first stage (normalization) preprocesses the input image in order to reduce letter position uncertainty; the second stage (feature extraction) is based on the feedforward model of orientation selectivity; the third stage (letter pre-recognition) is based on a convolutional neural network, and the last stage (word recognition) is based on the interactive activation model.

Published
2008
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