The short answer is "no", but a fuller answer is that my use case is a bit
different from others and is as follows ...

I trained tesseract to read the MICR line at the bottom of bank checks
using only 20K checks (i.e. real data, not synthetic).  I was able to get
85% accuracy where the reason for about 13% of the failures was that the
person's signature overlapped the MICR line.  If I could figure out a way
to detect and remove the overlapping signature contours, then I think I
would be able to reach 98% accuracy.  Any suggestions?  I don't know if
tesseract would ever be able to do this alone.

I also tried training tesseract from scratch using synthetic data but have
not yet achieved the same accuracy.  I think the problem is that the
synthetic data doesn't simulate real data closely enough.

On Tue, Nov 14, 2023 at 12:55 AM Des Bw <desaleg...@gmail.com> wrote:

> It looks like every one is having issues with tesseract. I am not able to
> find any one who has a great success with this software.
> It would be really encouraging to hear any success story from
> any language.
>
> Has anybody a successful training of tesseract?
> (like, a model that can detect with higher accuracy: 98% or more ?)
>
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