Engineering color barcode algorithms for mobile applications

The wide availability of on-board cameras in mobile devices and the increasing demand for higher capacity have recently sparked many new color barcode designs. Unfortunately, color barcodes are much more prone to errors than black and white barcodes, due to the chromatic distortions introduced in the printing and scanning process. This is a severe limitation: the higher the expected error rate, the more redundancy is needed for error correction (in order to avoid failures in barcode reading), and thus the lower the actual capacity achieved. Motivated by this, we design, engineer and experiment algorithms for decoding color barcodes with high accuracy. Besides tackling the general trade-off between error correction and data density, we address challenges that are specific to mobile scenarios and that make the problem much more complicated in practice. In particular, correcting chromatic distortions for barcode pictures taken from phone cameras appears to be a great challenge, since pictures taken from phone cameras present a very large variation in light conditions. We propose a new barcode decoding algorithm based on graph drawing methods, which is able to run in few seconds even on low-end computer architectures and to achieve nonetheless high accuracy in the recognition phase. The main idea of our algorithm is to perform color classification using force-directed graph drawing methods: barcode elements which are very close in color will attract each other, while elements that are very far will repulse each other.