Training Neural Networks for Reading Handwritten Amounts on Checks
Name
Training Neural Networks for Reading 4365-02.pdf
Size
332.49 KB
Format
Adobe PDF
Checksum (MD5)
5c174e74cc7bf7378646215177433a7d
Author(s)
Palacios, Rafael
Gupta, Amar
Date Issued
June 7, 2002
Series/Report no.
MIT Sloan School of Management Working Paper;4365-02
Abstract
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.
Subjects
Neural Networks
Optical Character Recognition
Check Processing
Document Imaging
Unconstrained Handwritten Numerals
Persistent DSpace Link
