Neural Network-Based Price Tag Data Analysis

Pavel Laptev, Sergey Litovkin, Sergey Davydenko, Anton Konev, Evgeny Kostyuchenko and Alexander Shelupanov
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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

Future Internet, 2022, vol. 14, issue 3, 1-14

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%.

Keywords: image segmentation; OCR; YOLOv4-tiny; neural networks; UNet; MobileNetV2; VGG16; EasyOCR API; price tag (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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