Knowledge guided adaptive binarization for 2D barcode images captured by mobile phones
In this paper, we investigate the problem of binarization of the 2D barcode images captured by mobile devices. The poor quality of the images due to noise, nonuniform illumination and inherent limitation and distortion of the camera makes the task of binarization more challenging. The global thresholding techniques employed by most existing approaches do not work well for the barcode images captured under uncontrolled conditions. Hence, we propose an adaptive binarization technique by taking the prior knowledge such as the edge structure and maximum element width of the 2D barcode into consideration. Incrementally updating the elements in a moving window is proposed to expediate the process for mobile phone applications. Comparisons with other binarization methods showthe superior performance of our method, which achieves about 96.6% recognition rate on a dataset of 787 images.