I’ve hit somewhat of a brick wall. I managed to get some morphological hit&miss code that thinned the images down to 1px wide and it worked fine on the previously posted test. However after that I started getting horrible loops and things attached to the digits ruining all chance of OCR from them. Coupled with the fact that additional photos taken with an actual iPhone camera as opposed to my Lumix don’t come out too well it’s a bit of a dead loss right now. On the plus side I have implemented my own code to write out bitmap files so once I’ve got my thresholded quantized image I’m free of MagickWand. Potentially I can implement the magic wand stuff myself now too and do away with it completely. However these problems are in dire need of solving:
1) Identifying some criteria upon which to threshhold and image (currently what radius to use in the process). Minor changes make the world of difference although 40-45px is still somewhat optimal.
2) Some way of RIPing the image. (Remove Isolated Pixels).
3) Some way of perhaps filling in the obvious holes, morphological thickening sucks.
4) A better approach to thinning, perhaps, if 2&3 don’t help with the current method.
5) A corner detection algorithm that works and isn’t either: (c)Dera/QinetiQ, Some Student or Patented and aww screw let’s face it, I need to do my own.
On the plus side a trip to the Central Resources Library for Hertfordshire has yielded useful material.