Neural Network-Based Price Tag Data Analysis

Neural Network-Based Price Tag Data Analysis

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. Addit...

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Veröffentlicht in:Future internet 2022-03, Vol.14 (3), p.88
Hauptverfasser: Laptev, Pavel, Litovkin, Sergey, Davydenko, Sergey, Konev, Anton, Kostyuchenko, Evgeny, Shelupanov, Alexander
Format: Artikel
Sprache:eng
Schlagworte:
Accuracy
Algorithms
Bar codes
Data analysis
Employees
Image segmentation
Internet
MobileNetV2
Neural networks
Object recognition
OCR
Software
UNet
YOLOv4-tiny
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Beschreibung
Zusammenfassung: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%.
ISSN:1999-5903
1999-5903
DOI:10.3390/fi14030088