Newsgroups: comp.ai.neural-nets Path: utzoo!utgpu!news-server.csri.toronto.edu!rpi!zaphod.mps.ohio-state.edu!van-bc!ubc-cs!alberta!arms From: arms@cs.UAlberta.CA (Bill Armstrong) Subject: Re: Digital Character Recognition Message-ID: Keywords: Digit Recognition Sender: news@cs.UAlberta.CA (News Administrator) Organization: University of Alberta, Edmonton, Canada References: <1991Apr26.082505.11860@images.cs.und.ac.za> Date: Wed, 1 May 1991 23:36:30 GMT 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