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Lightweight neural network-based pipeline for barcode image preprocessing
P.K. Zlobin 1,2, V.A. Karnaushko 1, D.M. Ershova 1, R. Sánchez-Rivero 3, P.V. Bezmaternykh 1,2, D.P. Nikolaev 1,2

1 Smart Engines Service LLC,
117312, Russia, Moscow, 60-th Anniversary of October avenue, 9;
2 Federal Research Center “Computer Science and Control” of RAS,
119333, Russia, Moscow, Vavilova st., 44 b. 2;
3 Advanced Technologies Application Center (CENATAV),
Playa P.C.12200, Havana, Cuba, 7A, #21406 Siboney

 PDF, 1566 kB

DOI: 10.18287/COJ1759

Pages: 1071-1080.

Full text of article: English language.

Abstract:
Barcode scanning greatly benefited from deep learning research, as well as the image processing stages included in its workflow. These stages commonly handle pre-processing tasks like localizing barcode symbols in the input image, identifying their type, and normalizing the found regions. They are especially important when there is no a priori knowledge of input image capturing conditions. Thus, a case of multiple barcode recognition within a unique image drastically differs from a single barcode processing in video stream via smartphone. We assess how accuracy of these stages affects the accuracy of the whole barcode scanning as its best and propose a lightweight neural network-based pipeline implementing tasks listed above. To perform this assessment and evaluate the performance of the proposed pipeline elements, we conduct a series of experiments using the set of popular open source scanners, including OpenCV, WeChat, ZBar, ZXing and ZXing-cpp over the SE-barcode and Dubska datasets. These experiments reveal how the proposed pipeline can be configured for optimum speed and accuracy performance depending on the objective and the chosen scanner.

Keywords:
barcode scanning, image processing, deep learning.

Citation:
Zlobin PK, Karnaushko VA, Ershova DM, Sánchez-Rivero R, Bezmaternykh PV, Nikolaev DP. Lightweight neural network-based pipeline for barcode image preprocessing. Computer Optics 2025; 49(6): 1071-1080. DOI: 10.18287/COJ1759.

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