Training Neural Networks for Reading Handwritten Amounts on Checks
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.
|Date of creation:||07 Jun 2002|
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- Kirstin E. Wells, 1996. "Are checks overused?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall, pages 2-12.
- Palacios, Rafael & Wang, Patrick S.P. & Gupta, Amar, 2002. "Reading Courtesy Amounts on Handwritten Paper Checks," Working papers 4364-02, Massachusetts Institute of Technology (MIT), Sloan School of Management.
- Joanna Stavins, 1997. "A comparison of social costs and benefits of paper check presentment and ECP with truncation," New England Economic Review, Federal Reserve Bank of Boston, issue Jul, pages 27-44.
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