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dc.creatorPalacios, Rafael
dc.creatorGupta, Amar
dc.date2002-06-07T18:31:17Z
dc.date2002-06-07T18:31:17Z
dc.date2002-06-07T18:31:26Z
dc.date.accessioned2012-06-07T18:55:55Z
dc.date.available2012-06-07T18:55:55Z
dc.date.issued2012-06-07
dc.identifierhttp://hdl.handle.net/1721.1/699
dc.identifier.urihttps://repositorio.leon.uia.mx/xmlui/1721.1/699
dc.descriptionWhile 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.
dc.format340466 bytes
dc.formatapplication/pdf
dc.languageen_US
dc.relationMIT Sloan School of Management Working Paper;4365-02
dc.subjectUnconstrained Handwritten Numerals
dc.subjectCheck Processing
dc.subjectDocument Imaging
dc.subjectNeural Networks
dc.subjectOptical Character Recognition
dc.titleTraining Neural Networks for Reading Handwritten Amounts on Checks


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