An integrated deep-learning and geometric approach to 1D barcode

Yunzhe Xiao; Junxin Jiang; Kai Xu

doi:10.1117/12.2540364

31 July 2019 An integrated deep-learning and geometric approach to 1D barcode

Yunzhe Xiao, Junxin Jiang, Kai Xu

Proceedings Volume 11198, Fourth International Workshop on Pattern Recognition; 1119811 (2019) https://doi.org/10.1117/12.2540364
Event: Fourth International Workshop on Pattern Recognition, 2019, Nanjing, China

Abstract

Vision-based 1D barcode reading gains increasing research due to great demand of high degree of automation. Aiming at detecting image region of 1D barcodes, existing geometric approaches barely balance speed and precision. Deeplearning- based methods can locate 1D barcode fast but lack effective and accurate segmentation process, while pure geometric-based methods take unnecessary computational cost when processing high resolution image. We propose to integrate the deep-learning and geometric approaches, to tackle robust barcode localization in the presence of complicated background and accurate barcode detection within the localized region, respectively. Our integrated solution benefits the complementary advantages of the two methods. Through extensive experiments on standard benchmarks, we show our integrated approach outperforms the state-of-the-arts by at least 5 percentages.

Citation Download Citation

Yunzhe Xiao, Junxin Jiang, and Kai Xu "An integrated deep-learning and geometric approach to 1D barcode", Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 1119811 (31 July 2019); https://doi.org/10.1117/12.2540364

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KEYWORDS

Image segmentation

Image processing

Databases

Distortion

Image processing algorithms and systems

Neural networks

Performance modeling

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