arms@cs.UAlberta.CA (Bill Armstrong) (05/06/91)
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 -- comp.archives file verification 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