Abstract:
In complex illumination conditions, the images collected by two dimensional code scanner are prone to general highlighting with shade and part highlighting with shade, which make it difficult to determine the image segmentation threshold. In Sauvola algorithm, the effect of the window size
w value and the correction factor
k value on the quick response (QR) code image binarization was studied. Aiming at the shortcomings, such as the poor anti-noise ability of the global binarization method and the slow processing speed of the local binarization method, an improved QR code image binarization method was proposed, which combined the Otsu and Sauvola algorithms to improve the algorithm anti-noise ability, and used the integral graph to improve the algorithm operation efficiency. Experiments show that the binarization effect of this method is better than the classical binarization method, and the average running efficiency is 17 times higher than that of the original Sauvola algorithm, which increases the recognition success rate.
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