Abstract

http://ssrn.com/abstract=314779
 
 

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Training Neural Networks for Reading Handwritten Amounts on Checks


Rafael Palacios


Pontifical University Comillas of Madrid

Amar Gupta


Pace University - The Seidenberg School of Computer Science and Information Systems

May 2002

MIT Sloan Working Paper No. 4365-02; Eller College Working Paper No. 1022-05

Abstract:     
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.

Number of Pages in PDF File: 8

Keywords: Optical Character Recognition, Neural Networks, Document Imaging, Check Processing, Unconstrained Handwritten Numerals

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Date posted: June 1, 2002  

Suggested Citation

Palacios, Rafael and Gupta, Amar, Training Neural Networks for Reading Handwritten Amounts on Checks (May 2002). MIT Sloan Working Paper No. 4365-02; Eller College Working Paper No. 1022-05. Available at SSRN: http://ssrn.com/abstract=314779 or http://dx.doi.org/10.2139/ssrn.314779

Contact Information

Rafael Palacios (Contact Author)
Pontifical University Comillas of Madrid ( email )
Alberto Aguilera 21
Madrid, Madrid 28015
Spain
Amar Gupta
Pace University - The Seidenberg School of Computer Science and Information Systems ( email )
163 William Street
New York, NY 10038
United States
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