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Open AccessArticle

1D Barcode Detection via Integrated Deep-Learning and Geometric Approach

by 1 and 2,*
College of Computer Science, National University of Defense Technology, Changsha 410000, China
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518000, China
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(16), 3268;
Received: 29 June 2019 / Revised: 24 July 2019 / Accepted: 6 August 2019 / Published: 9 August 2019
(This article belongs to the Special Issue Computer Vision and Pattern Recognition in the Era of Deep Learning)
Vision-based 1D barcode reading has been the subject of extensive research in recent years due to the high demand for automation in various industrial settings. With the aim of detecting the image region of 1D barcodes, existing approaches are both slow and imprecise. Deep-learning-based methods can locate the 1D barcode region fast but lack an adequate and accurate segmentation process; while simple geometric-based techniques perform weakly in terms of localization and take unnecessary computational cost when processing high-resolution images. We propose integrating the deep-learning and geometric approaches with the objective of tackling robust barcode localization in the presence of complicated backgrounds and accurately detecting the barcode within the localized region. Our integrated real-time solution combines the advantages of the two methods. Furthermore, there is no need to manually tune parameters in our approach. Through extensive experimentation on standard benchmarks, we show that our integrated approach outperforms the state-of-the-art methods by at least 5%. View Full-Text
Keywords: 1D barcode; barcode detection; deep learning; object detection; line segment detection 1D barcode; barcode detection; deep learning; object detection; line segment detection
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MDPI and ACS Style

Xiao, Y.; Ming, Z. 1D Barcode Detection via Integrated Deep-Learning and Geometric Approach. Appl. Sci. 2019, 9, 3268.

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