Hi Dmitri/Partik,
Thanks for your reply.I am sending along the pre processed test image which
I had mentioned in my response.
tesseract output - SOUTHBURY~ CT DLUBB


Regards,
Amrit.

On Wed, Apr 6, 2011 at 12:05 AM, Dmitri Silaev <[email protected]>wrote:

> Agree not to use dictionary at all. IMO the best you can do is:
> - use appropriate whitelists for each character position
> - obtain a set of char choices for every char position
> - restrict choice sets by using other semantic information you may have
>
> Warm regards,
> Dmitri Silaev
>
>
>
>
>
> On Wed, Apr 6, 2011 at 6:00 AM, Amrit <[email protected]> wrote:
> > Hi All,
> >        I am trying to evaluate tesseract to decode US postal address
> > from a set of images(english text with varying font).I want to extract
> > the city,state zipcode combination from the image.In doing so, out of
> > the box tesseract 3.01 performance is average and I would like to
> > increase the accuracy of the system by providing a custom grammar/
> > wordlist (language model).
> >       Any idea as to how to accomplish this?(My custom grammar/
> > language model will only contain City,State and ZipCode numbers).
> >
> > I have tried to create custom dawg by following on the lines of
> > 'training tesseract 3' wiki page, but this doesn't seem to work at
> > all.Is there any way I can do this without training a subset of my
> > test images?
> >
> > Regards,
> > Amrit.
> >
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