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Archive-name: ai/neural-nets/atree/1991-05-01
Archive: menaik.cs.ualberta.ca:pub/atree.tar.Z [129.128.4.241]
Original-posting-by: arms@cs.UAlberta.CA (Bill Armstrong)
Original-subject: Re: Digital Character Recognition
Reposted-by: emv@msen.com (Edward Vielmetti, MSEN)

garydean@images.cs.und.ac.za writes:

>I'm currently studying for my Computer Science Honours and would like to use
>neural nets to solve the problem of digit recognition. 
...

>I have been reading the volumes available from the PDP research group. I have
>tentatively decided to use back propogation but would like any form of comment 
>or references to help me. 
...

>Gary Nicholson. 

I have used adaptive logic networks for OCR.  They were tested on the
Highleyman data from the US Post Office, which had handwritten
numerals 0 - 9, as you intend to use.  The logic networks proved to be
quite immune to salt-and-pepper noise and rotation of synthesized
characters, so I'm sure you would have no problems in making an OCR
system with them.  I suspect the system would be faster than a
backpropagation network both for learning and execution.

The code is available by ftp from menaik.cs.ualberta.ca
[129.128.4.241] in pub/atree.tar.Z.  Here is a reference with some
experiments on noise immunity and rotation, done with a less powerful
early adaptive algorithm.

W. Armstrong and J. Gecsei, "Adaptation Algorithms for
Binary Tree Networks", IEEE Trans. on Systems, Man and
Cybernetics, 9, 1979, pp. 276-285.

--
***************************************************
Prof. William W. Armstrong, Computing Science Dept.
University of Alberta; Edmonton, Alberta, Canada T6G 2H1
arms@cs.ualberta.ca Tel(403)492 2374 FAX 492 1071

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menaik.cs.ualberta.ca
-rw-r--r--  1 556      17         154327 May  1 10:17 pub/atree.tar.Z
found atree ok
menaik.cs.ualberta.ca:pub/atree.tar.Z