Subject: RE: OCR
From: simo <s@ssimo.org>
Date: Mon, 12 Jun 2006 16:06:29 -0400

This made me remember that I saw some work on patter recognition that
uses neural networks and Bayesian inference with very promising results,
seem OCR would probably benefit from such approach.

Simo.

On Mon, 2006-06-12 at 11:03 -0600, Anderson, Kelly wrote:
> I can see where that form of mathematics would be very useful in the
> problem domain. In the "olden days" of OCR, the same was achieved with
> Octal Trees. Probably not as elegant, but the ideas have been around
> forever.
> 
> Progress in Pattern Recognition is very slow. For example, when I took
> my graduate level pattern recognition class in 1987, we used a book
> published in Russia in 1958. It's still one of the better text books out
> there.
> 
> My teacher was rather fond of showing how everything really is the same
> thing. Just to show us one day, he did a mathematical proof that Baysian
> Classification was mathematically equivalent to Neural Network
> classification. I can't remember any of the math, and I'm not sure I
> understood it entirely then, but it was fascinating and intuitive to see
> that it was all really the same stuff...
> 
> -Kelly
> 
> -----Original Message-----
> Anderson, Kelly wrote:
> > Wavelets can be used in Segmentation and Feature Extraction. They may 
> > be very good for segmentation,
> 
> There's been work lately on hierarchical Feature Extraction, where an
> image is analysed, effectively at different resolutions, to extract
> features and their relationships in a scale independent way.  That's not
> for OCR specifically, but for computer vision more generally.
> Cleverly the results are scale independent even though the set of
> resolutions analysed is discrete.  The transforms looked suspiciously
> similar to wavelets to me.
> 
> -- Jamie
> 
> 
> 
> 
> 
> 
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