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Decoding 1-D Barcode from Degraded Images Using a Neural Network

  • Conference paper
Computer Vision, Imaging and Computer Graphics. Theory and Applications (VISIGRAPP 2010)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 229))

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Abstract

Today we know that billions of products carry the 1-D bar codes, and with the increasing availability of camera phones, many applications that take advantage of immediate identification of the barcode are possible. The existing open-source libraries for 1-D barcodes recognition are not able to recognize the codes from images acquired using simple devices without autofocus or macro function. In this article we present an improvement of an existing algorithm for recognizing 1-D barcodes using camera phones with and without autofocus. The multilayer feedforward neural network based on backpropagation algorithm is used for image restoration in order to improve the selected algorithm. Performances of the proposed algorithm were compared with those obtained from available open-source libraries. The results show that our method makes possible the decoding of barcodes from images captured by mobile phones without autofocus.

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Author information

Authors and Affiliations

  1. Università degli Studi dell’Insubria, via Ravasi 2, Varese, Italy

    A. Zamberletti, I. Gallo, M. Carullo & E. Binaghi

Authors
  1. A. Zamberletti
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  2. I. Gallo
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  3. M. Carullo
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  4. E. Binaghi
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Editor information

Editors and Affiliations

  1. LISA-ISTIA, Université d’Angers, 62, avenue Notre-Dame du Lac, 49000, Angers, France

    Paul Richard

  2. Departamento de Sistemas e Informática, Escola Superior de Tecnologia do IPS, Rua do Vale de Chaves Estefanilha, 2910, Setúbal, Portugal

    José Braz

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Zamberletti, A., Gallo, I., Carullo, M., Binaghi, E. (2011). Decoding 1-D Barcode from Degraded Images Using a Neural Network. In: Richard, P., Braz, J. (eds) Computer Vision, Imaging and Computer Graphics. Theory and Applications. VISIGRAPP 2010. Communications in Computer and Information Science, vol 229. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25382-9_3

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  • DOI: https://doi.org/10.1007/978-3-642-25382-9_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25381-2

  • Online ISBN: 978-3-642-25382-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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