Robust Angle Invariant 1D Barcode Detection

Zamberletti, Alessandro; Gallo, Ignazio; Albertini, Simone

doi:10.1109/acpr.2013.17

Cited by 42 publications

(34 citation statements)

References 14 publications

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“…The work of Cresot et al, 2015 [7] is a solid baseline for 1D barcode detection. They evaluated their approach on Muenster and on extended ArTe-Lab 1D Medium barcode database (Artelab) provided by Zamberletti et al [8] outperforming him on both datasets. The solution in [7] seems to outperform [6] despite it is hard to compare as they were evaluated on different datasets using slightly different metrics.…”

Section: Related Workmentioning

confidence: 99%

Universal Barcode Detector via Semantic Segmentation

Zharkov

Zagaynov

2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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Barcodes are used in many commercial applications, thus fast and robust reading is important. There are many different types of barcodes, some of them look similar while others are completely different. In this paper we introduce new fast and robust deep learning detector based on semantic segmentation approach. It is capable of detecting barcodes of any type simultaneously both in the document scans and in the wild by means of a single model. The detector achieves state-of-the-art results on the ArTe-Lab 1D Medium Barcode Dataset with detection rate 0.995. Moreover, developed detector can deal with more complicated object shapes like very long but narrow or very small barcodes. The proposed approach can also identify types of detected barcodes and performs at real-time speed on CPU environment being much faster than previous state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Universal Barcode Detector via Semantic Segmentation

Zharkov

Zagaynov

2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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“…These methods are accurate in 1D barcode region extraction and perform well in benchmark datasets such as Zamberletti et al [5], with the 1D barcodes images captured in a close distance; however, there still exists a localization problem. In various industrial applications, it is necessary to process a high-resolution image where 1D barcodes may appear in a small region, which makes localization of the 1D barcodes difficult especially when the background texture is complex.…”

Section: Related Workmentioning

confidence: 99%

“…Existing approaches to vision-based barcode reading are either geometric-based [2][3][4] or learning-based [5,6]. While geometric approaches perform better in terms of accurateness of segmentation, they tend to be weak in the presence of severe distortion or occlusion.…”

Section: Introductionmentioning

confidence: 99%

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

Xiao

Ming

2019

Applied Sciences

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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%.

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“…The method can identify rotated or defocused barcode images. Another work has been done on making this system angle invariant and less or not at all user interactive [9].…”

Section: Detection Policy From Previous Workmentioning

confidence: 99%

A Study on Multiple Barcode Detection from an Image in Business System

Chowdhury¹,

Rahman²,

Sakib³

2019

IJCA

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The pillar of automatic identification is Barcode technology which is used comprehensively in real time applications with various types of codes. The different types of codes and applications sometimes faces special problems, so the improvement of the effectiveness must be done persistently. This paper's proposal mainly deals with multiple product's barcode to be detected simultaneously. The proposed algorithm which will have implementation for supershop billing system and inventory management. The method will recognize the barcodes using image processing. Images will be taken using mobile camera sensor. It will detect 1D barcodes such as EAN-13 barcodes, Code-128 barcodes, 2D barcodes such as QR codes. Moreover, it will be angle invariant, requires less user interaction than usual and can be executed on available computers. This model helps consumers to minimize the time for the billing system in shopping. We have implemented this in Python IDE using OpenCV library.

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Robust Angle Invariant 1D Barcode Detection

Cited by 42 publications

References 14 publications

Universal Barcode Detector via Semantic Segmentation

Universal Barcode Detector via Semantic Segmentation

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

A Study on Multiple Barcode Detection from an Image in Business System

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