Generative approach for 1D barcode dataset population for mobile-based recognition

A. N. Milovzorov; P. V. Bezmaternykh; V. V. Arlazarov

doi:10.1117/12.2587628

4 January 2021 Generative approach for 1D barcode dataset population for mobile-based recognition

A. N. Milovzorov, P. V. Bezmaternykh, V. V. Arlazarov

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 116051P (2021) https://doi.org/10.1117/12.2587628
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

Barcode recognition via the mobile device camera is an actual problem which occurs in various fields. To receive experimental baselines and provide methods comparison researchers require some carefully annotated data samples. For those purposes, some datasets have already been collected and published in the public domain. But they suffer from different disadvantages. In this paper, we present a novel challenging dataset designed for barcode reading quality evaluation. It is populated using the generative approach with well-established image augmentation technique application. Among them, different geometrical transformations, kind of noises, brightness and lighting variance were used. The generative approach also allowed to populate ground-truth automatically and exclude errors introduced during the manual process. The dataset consists of training (41184 images) and validation parts (10296 images). It contains seven most popular symbologies: CODABAR, CODE-39, CODE-93, CODE-128, EAN-13, UPC-A, UPC-E. As an experimental baseline, the reading quality obtained with the open-source Zxing library is provided. The dataset with all the supplementary materials is available at ftp://smartengines.com/barcode.

Citation Download Citation

A. N. Milovzorov, P. V. Bezmaternykh, and V. V. Arlazarov "Generative approach for 1D barcode dataset population for mobile-based recognition", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 116051P (4 January 2021); https://doi.org/10.1117/12.2587628

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