NEURAL IMAGE RESTORATION FOR DECODING 1-D BARCODES USING COMMON CAMERA PHONES

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

Abstract

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.

References

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Paper Citation


in Harvard Style

Zamberletti A., Gallo I., Carullo M. and Binaghi E. (2010). NEURAL IMAGE RESTORATION FOR DECODING 1-D BARCODES USING COMMON CAMERA PHONES . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-028-3, pages 5-11. DOI: 10.5220/0002811600050011


in Bibtex Style

@conference{visapp10,
author={A. Zamberletti and I. Gallo and M. Carullo and E. Binaghi},
title={NEURAL IMAGE RESTORATION FOR DECODING 1-D BARCODES USING COMMON CAMERA PHONES},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={5-11},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002811600050011},
isbn={978-989-674-028-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)
TI - NEURAL IMAGE RESTORATION FOR DECODING 1-D BARCODES USING COMMON CAMERA PHONES
SN - 978-989-674-028-3
AU - Zamberletti A.
AU - Gallo I.
AU - Carullo M.
AU - Binaghi E.
PY - 2010
SP - 5
EP - 11
DO - 10.5220/0002811600050011