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...
| Main Authors: | , |
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| Language: | en_US |
| Published: |
2002
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/699 |
| Summary: | 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. |
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