A unified probabilistic framework for robust decoding of linear barcodes

Both consumer market and manufacturing industry makes heavy use of 1D (linear) barcodes. From helping the visually impaired to identifying the products to industrial automated industry management, barcodes are the prevalent source of item tracing technology. Because of this ubiquitous use, in recent years, many algorithms have been proposed targeting barcode decoding from high-accessibility devices such as cameras. However, the current methods have at least one of the two major problems: 1) they are sensitive to blur, perspective/ lens distortions, and non-linear deformations, which often occur in practice, 2) they are specifically designed for a specific barcode symbology (such as UPC-A) and cannot be applied to other symbologies. In this paper, we aim to address these problems and present a dynamic Bayesian network in order to robustly model all kinds of linear progressive barcodes. We apply our method on various barcode datasets and compare the performance with the state-of-the-art. Our experiments show that, as well as being applicable to all progressive barcode types, our method provides competitive results in clean UPC-A datasets and outperforms the state-of-the-art in difficult scenarios.