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copied!<p>I'm Martin's friend who was working on this earlier this year. This was my first ever coding project, and kinda ended in a bit of a rush, so the code needs some errr...decoding... I'll give a few tips from what I've seen you doing already, and then sort my code on my day off tomorrow.</p> <p>First tip, <code>OpenCV</code> and <code>python</code> are awesome, move to them as soon as possible. :D</p> <p>Instead of removing small objects and or noise, lower the canny restraints, so it accepts more edges, and then find the largest closed contour (in OpenCV use <code>findcontour()</code> with some simple parameters, I think I used <code>CV_RETR_LIST</code>). might still struggle when it's on a white piece of paper, but was definitely providing best results. </p> <p>For the <code>Houghline2()</code> Transform, try with the <code>CV_HOUGH_STANDARD</code> as opposed to the <code>CV_HOUGH_PROBABILISTIC</code>, it'll give <em>rho</em> and <em>theta</em> values, defining the line in polar coordinates, and then you can group the lines within a certain tolerance to those. </p> <p>My grouping worked as a look up table, for each line outputted from the hough transform it would give a rho and theta pair. If these values were within, say 5% of a pair of values in the table, they were discarded, if they were outside that 5%, a new entry was added to the table.</p> <p>You can then do analysis of parallel lines or distance between lines much more easily.</p> <p>Hope this helps.</p>

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