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January 2008
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2008.01.28 17:43 "OMR bubble detection (Leonardo Serni)", by David Abrames
Dear Leonardo, Thank you for your reply > The most practical approach would be, I think, to employ grayscale images > and calculate the minimum white and maximum black threshold... then count > the number of "black" pixels in each bubble. Empty and full balloons will > sport different blackness averages. That is a good suggestion. The TIFF images are bitonal and I was planning on simply counting the number of black pixels in the bubble and once I reached 80% then count that has a 'Mark'. If the count was less than 80% but greater than 40% that would be "not sure" and below 40% is not marked. > Things get a bit more complicated if images aren't aligned (but still lay > parallel to axes, i.e., you have an offset on X and Y) and may get really > hairy if you need to cope with rotations (even if for very small rotation > values you might get off by employing shearing). Very true, I already have despeckel and deskew routines from another project so what I was looking for was a best method for locating the bubbles on the page. I guess I could simply reconstruct the page in memory and then start reading pixels line by line until the page was done and then analyze the data and from that make a determination if a bubble is marked or not marked. This seems like such a brute force method that I figured there might be a better way. > A much more complicated approach would require correlation between a full > balloon and an empty one, but this assumes people mark consistently their > balloons, which, let's face it, is not a realistic constraint. Yeah I think I want to stay away from this, at least for now. Regards David