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Proceedings Paper

An integrated deep-learning and geometric approach to 1D barcode
Author(s): Yunzhe Xiao; Junxin Jiang; Kai Xu
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Paper 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.

Paper Details

Date Published: 31 July 2019
PDF: 6 pages
Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 1119811 (31 July 2019); doi: 10.1117/12.2540364
Show Author Affiliations
Yunzhe Xiao, National Univ. of Defense Technology (China)
Junxin Jiang, National Univ. of Defense Technology (China)
Kai Xu, National Univ. of Defense Technology (China)

Published in SPIE Proceedings Vol. 11198:
Fourth International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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