In this paper the binarization of matrix codes is investigated as an application of supervised learning of image processing tasks using a recurrent version of the Neural Abstraction Pyramid. The desired network output is computed using an adaptive thresholding method for undegraded images. The network is trained to iteratively produce the same output even when the contrast is lowered and typical noise is added to the input. The network discovers the structure of the codes and uses it for binarization. This makes the recognition of degraded matrix codes possible for which adaptive thresholding fails
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