Training Neural Networks for Reading Handwritten Amounts on Checks
Universidad Pontificia Comillas
Massachusetts Institute of Technology (MIT)
MIT Sloan Working Paper No. 4365-02; Eller College Working Paper No. 1022-05
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.
Number of Pages in PDF File: 8
Keywords: Optical Character Recognition, Neural Networks, Document Imaging, Check Processing, Unconstrained Handwritten Numerals
Date posted: June 1, 2002
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