Abstract
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%.
Highlights
The detection of 1D barcodes has applications in sectors such as production, retailing, logistics, and transportation
The robust automated 1D barcode detection approach aims at detecting a pure 1D barcode region and rotation angle through sensor vision
The rotation angle θ was obtained in Section 4; we still need an approach to remove the line segments which do not belong to the barcode region and select the correct line segments for region proposal
Summary
The detection of 1D barcodes has applications in sectors such as production, retailing, logistics, and transportation. The right rotation angle ensures the correctness of the decoding result, while accurate region segmentation contributes to faster decoding for barcode decoding tools (such as ZXing library [1]) and image distortion restoration. Existing approaches to vision-based barcode reading are either geometric-based [2,3,4] or learning-based [5,6]. Segmentation neural networks can output the segmentation region quickly, but even a tiny defect in the 1D barcode region can cause a completely different decoding result, which often occurs in real-world scenarios. We propose a novel 1D barcode detection approach using integrated deep-learning and geometric methods in two-stages: barcode location and region extraction. We show that our approach is faster than the existing approaches
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