8 Pages Posted: 1 Jun 2002
Date Written: May 2002
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.
Keywords: Optical Character Recognition, Neural Networks, Document Imaging, Check Processing, Unconstrained Handwritten Numerals
Suggested Citation: 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: https://ssrn.com/abstract=314779 or http://dx.doi.org/10.2139/ssrn.314779