I am writing something that needs to verify the practical readability of qr codes which has been painted over.
First thing that comes to mind is to apply Gaussian blur on the image and check if it is readable. Then I could get a “readability score” by doing binary search between blur strengths range [0, n].
I also may do the same with noise, not sure if I should.
Would appreciate if somebody with more knowledge of QR codes help me
QR codes come with (differing levels of) error correction. You could decode it and take the amount of corrected errors as a score.
I don’t know what unit/chunk of data the error correction works on but you could alternatively dive a bit into information theory and calculate how close any of the chunks are to an uncorrectable error and use that as a score.
Since the first uncorrectable error will cause the scan to fail this would match your problem definition fairly well.
One benefit of this approach would be that it’s a single scan and done vs searching n permutations of the same code.
Or maybe you’d want to scan it a couple of times to rule out any additional errors introduced by the camera or environment.I doubt Gaussian blur is an accurate model of real-world situations.
At the end of the day if you are worried about the codes being painted over print a few out and paint over them. Then scan with a variety of scanners.
If I had to come up with some more digital tests I would guess that a few of these are more representative of real-world situations:
- Lower contrast. For example lighten or darken the whole code. This would simulate things like scanning in low light or with glare.
- Block out sections of the code. This will test error correction levels and simulate partial damage or pockets of extreme glare.
- Skew the code in various ways. This simulates the perspective shift of people scanning the code from an angle.
Ideally combine them in a bunch of scenarios then try to scan with a variety of scanner implementations.
