kilmer@hq.af.mil (09/19/90)
I am looking for information on which learning architecture is best suited
for feature extraction. I know it's hard to judge one from another except
in individual cases...so I'll briefly give you a summary of what I'm trying
to do.
I've been delving into the various aspects of OCR. I first started by
creating a backprop net and training it on a font consisting of 94 characters.
The network converged on the solution, and was able to identify the 8x14
character matrix. It even worked well at identifying those characters with
5 to 10 percent noise. Unfortunately, it works on that size character only.
I was wondering how one would go about training a net to recognize features.
I think (although I'm very new to NN) that the hidden layers store features
in their particular weights and thresholds, or at least generalizations of
the input vectors...??? If this were the case, you could train the net with
characters that have similar features (such as a 'p' and a 'q') and have
those inputs match with a feature identifying output:
e.g: p-> 0010100 q-> 0001100
/ \ / \
Desc bar circle(arc) Desc bar circle(arc)
on left on right
of circle of circle
Would this work?? If so, what net would be best for this type of
association??
Also, what about feature deformation, translation, and size independence?
Any help would be appreciated.
Richard.
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| Richard Kilmer Kilmer@Opsnet-Pentagon.af.mil |
| VAX Systems Analyst (AKA Kilmer@26.24.0.26) |
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-------------------------------------------------------------------------.
| Richard Kilmer Kilmer@Opsnet-Pentagon.af.mil |
| VAX Systems Analyst (AKA Kilmer@26.24.0.26) |
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