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BarBeR: A Barcode Benchmarking Repository

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Pattern Recognition (ICPR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15317))

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Abstract

Since their invention in 1949, barcodes have remained the preferred method for automatic data capture, playing a crucial role in supply chain management. To detect a barcode in an image, multiple algorithms have been proposed in the literature, with a significant increase of interest in the topic since the rise of deep learning. However, research in the field suffers from many limitations, including the scarcity of public datasets and code implementations, which hampers the reproducibility and reliability of published results. For this reason, we developed “BarBeR” (Barcode Benchmark Repository), a benchmark designed for testing and comparing barcode detection algorithms. This benchmark includes the code implementation of various detection algorithms for barcodes, along with a suite of useful metrics. It offers a range of test setups and can be expanded to include any localization algorithm. In addition, we provide a large, annotated dataset of 8 748 barcode images, combining multiple public barcode datasets with standardized annotation formats for both detection and segmentation tasks. Finally, we share the results obtained from running the benchmark on our dataset, offering valuable insights into the performance of different algorithms.

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Notes

  1. 1.

    https://github.com/zxing/zxing.

  2. 2.

    https://github.com/ZBar/ZBar.

  3. 3.

    https://github.com/Henvezz95/BarBeR.

  4. 4.

    https://ditto.ing.unimore.it/barber.

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Acknowledgement

This work was supported by the University of Modena and Reggio Emilia and Fondazione di Modena, through the FAR 2023 and FARD-2023 funds (Fondo di Ateneo per la Ricerca).

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Authors and Affiliations

  1. University of Modena and Reggio Emilia, Modena, Italy

    Enrico Vezzali, Federico Bolelli & Costantino Grana

  2. Datalogic, S.p.A, Bologna, Italy

    Stefano Santi

Authors
  1. Enrico Vezzali
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  2. Federico Bolelli
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  3. Stefano Santi
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  4. Costantino Grana
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Corresponding author

Correspondence to Federico Bolelli .

Editor information

Editors and Affiliations

  1. University of Salford, Salford, Lancashire, UK

    Apostolos Antonacopoulos

  2. Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    Subhasis Chaudhuri

  3. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

  4. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  5. IIT Kharagpur, Kharagpur, West Bengal, India

    Saumik Bhattacharya

  6. Indian Statistical Institute Kolkata, Kolkata, West Bengal, India

    Umapada Pal

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Vezzali, E., Bolelli, F., Santi, S., Grana, C. (2025). BarBeR: A Barcode Benchmarking Repository. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15317. Springer, Cham. https://doi.org/10.1007/978-3-031-78447-7_13

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  • DOI: https://doi.org/10.1007/978-3-031-78447-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-78446-0

  • Online ISBN: 978-3-031-78447-7

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