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Training Neural Networks for Reading Handwritten Amounts on Checks

R. Palacios, A. Gupta

While reading handwritten text accurately is a difficult task for computers, the conversion of handwritten papers into digital format is necessary for automatic processing. Since most bank checks are handwritten, the number of checks is very high, and manual processing involves significant expenses, many banks are interested in systems that can read check automatically. This paper presents several approaches to improve the accuracy of neural networks used to read unconstrained numerals in the courtesy amount field of bank checks.


Palabras clave: Optical character recognition, neural networks, document imaging, check processing, unconstrained handwritten numerals.

2003 IEEE Workshop on Neural Networks for Signal Processing, ISBN:0-7803-8177-7, pp. 607-616, Toulouse (Francia). 17 septiembre 2003

DOI: DOI icon 10.1109/NNSP.2003.1318060    

Fecha de publicación: septiembre 2003.



Cita:
R. Palacios, A. Gupta, Training Neural Networks for Reading Handwritten Amounts on Checks, 2003 IEEE Workshop on Neural Networks for Signal Processing, ISBN:0-7803-8177-7, pp. 607-616. ISBN: 0-7803-8177-7, Toulouse, Francia, 17-19 Septiembre 2003


    Líneas de investigación:

IIT-02-048A

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