Show simple item record

dc.contributor.authorPalacios, Rafael
dc.contributor.authorGupta, Amar
dc.date.accessioned2002-06-07T18:31:17Z
dc.date.available2002-06-07T18:31:17Z
dc.date.issued2002-06-07T18:31:26Z
dc.identifier.urihttp://hdl.handle.net/1721.1/699
dc.description.abstractWhile 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. en
dc.format.extent340466 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesMIT Sloan School of Management Working Paper;4365-02
dc.subjectNeural Networksen
dc.subjectOptical Character Recognitionen
dc.subjectCheck Processingen
dc.subjectDocument Imagingen
dc.subjectUnconstrained Handwritten Numerals en
dc.titleTraining Neural Networks for Reading Handwritten Amounts on Checksen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record