Binarization of Low-Quality Barcode Images Captured by Mobile Phones Using Local Window of Adaptive Location and Size

Abstract

It is difficult to directly apply existing binarization approaches to the barcode images captured by mobile device due to their low quality. This paper proposes a novel scheme for the binarization of such images. The barcode and background regions are differentiated by the number of edge pixels in a search window. Unlike existing approaches that center the pixel to be binarized with a window of fixed size, we propose to shift the window center to the nearest edge pixel so that the balance of the number of object and background pixels can be achieved. The window size is adaptive either to the minimum distance to edges or minimum element width in the barcode. The threshold is calculated using the statistics in the window. Our proposed method has demonstrated its capability in handling the nonuniform illumination problem and the size variation of objects. Experimental results conducted on 350 images captured by five mobile phones achieve about 100% of recognition rate in good lighting conditions, and about 95% and 83% in bad lighting conditions. Comparisons made with nine existing binarization methods demonstrate the advancement of our proposed scheme.