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Training Neural Networks for Reading Handwritten Amounts on Checks

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  • Gupta, Amar
  • Palacios, Rafael

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.

Suggested Citation

  • Gupta, Amar & Palacios, Rafael, 2002. "Training Neural Networks for Reading Handwritten Amounts on Checks," Working papers 4365-02, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    • Handle: RePEc:mit:sloanp:699
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    File URL: http://hdl.handle.net/1721.1/699
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Kirstin E. Wells, 1996. "Are checks overused?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 20(Fall), pages 2-12.
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