Training Neural Networks for Reading Handwritten Amounts on Checks

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/699

Title:	Training Neural Networks for Reading Handwritten Amounts on Checks
Keywords:	Optical Character Recognition Unconstrained Handwritten Numerals Check Processing Document Imaging Neural Networks
Issue Date:	31-May-2013
Description:	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.
URI:	http://koha.mediu.edu.my:8181/jspui/handle/1721
Other Identifiers:	http://hdl.handle.net/1721.1/699
Appears in Collections:	MIT Items

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