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Neural Network-Based Price Tag Data Analysis

Author

Listed:
  • Pavel Laptev

    (Faculty of Security, Tomsk State University of Control Systems and Radioelectronics, 40 Lenina Prospect, 634050 Tomsk, Russia)

  • Sergey Litovkin

    (Faculty of Security, Tomsk State University of Control Systems and Radioelectronics, 40 Lenina Prospect, 634050 Tomsk, Russia)

  • Sergey Davydenko

    (Faculty of Security, Tomsk State University of Control Systems and Radioelectronics, 40 Lenina Prospect, 634050 Tomsk, Russia)

  • Anton Konev

    (Faculty of Security, Tomsk State University of Control Systems and Radioelectronics, 40 Lenina Prospect, 634050 Tomsk, Russia)

  • Evgeny Kostyuchenko

    (Faculty of Security, Tomsk State University of Control Systems and Radioelectronics, 40 Lenina Prospect, 634050 Tomsk, Russia)

  • Alexander Shelupanov

    (Faculty of Security, Tomsk State University of Control Systems and Radioelectronics, 40 Lenina Prospect, 634050 Tomsk, Russia)

Abstract

This paper compares neural networks, specifically Unet, MobileNetV2, VGG16 and YOLOv4-tiny, for image segmentation as part of a study aimed at finding an optimal solution for price tag data analysis. The neural networks considered were trained on an individual dataset collected by the authors. Additionally, this paper covers the automatic image text recognition approach using EasyOCR API. Research revealed that the optimal network for segmentation is YOLOv4-tiny, featuring a cross validation accuracy of 96.92%. EasyOCR accuracy was also calculated and is 95.22%.

Suggested Citation

  • Pavel Laptev & Sergey Litovkin & Sergey Davydenko & Anton Konev & Evgeny Kostyuchenko & Alexander Shelupanov, 2022. "Neural Network-Based Price Tag Data Analysis," Future Internet, MDPI, vol. 14(3), pages 1-14, March.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:3:p:88-:d:770195
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    References listed on IDEAS

    as
    1. Ahmad B A Hassanat, 2018. "Two-point-based binary search trees for accelerating big data classification using KNN," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-15, November.
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    Cited by:

    1. Yang Wang, 2023. "Advances Techniques in Computer Vision and Multimedia," Future Internet, MDPI, vol. 15(9), pages 1-2, September.

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