# Neural Network-Based Price Tag Data Analysis
## 5 Discussion  
In this article, a study was conducted to determine the optimal neural networks that will automate price tag data verification using mobile devices. The neural networks' (UNet, MobileNetV2, VGG16, YOLOv4-tiny) segmentation results were compared, and the results of the recognition of text located on the selected segments by EasyOCR were studied. Comparison of YOLOv4-tiny with SSD and the faster R-CNN showed the following results: SSD had an F1 score 0.67 and the faster R-CNN had an F1 score 0.66 according to article of [PERSON] [30], while our realization of YOLOv4-tiny had an F1 score of 0.61. However, various studies show that YOLOv4-tiny can outperform SSD [31; 32] and the faster R-CNN [24; 33]. Additionally, YOLOv4-tiny has a higher FPS, which leads to faster performance [24; 33]. Due to the fact that YOLOv4-tiny is a modified version of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Description** & **Rub** & **Kop** & **Rub\_Card** & **Kop\_Card** & **Barcode** & **Total Accuracy** \\ \hline Accuracy & 93.34\% & 100\% & 90\% & 100\% & 100\% & 88\% & 95.22\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy within a sample of 80 results.  
Figure 13: Client–server application results.  
YOLOv3, its accuracy increased [34], and YOLOv3 already outperformed SSD and faster R-CNN [35]. A mobile client-server application for price tag data verification was also developed based on the study's results.  
The study identified an optimal neural network for the segmentation of price tag images, namely YOLOv4-tiny, featuring an accuracy of 96.92%. This model is superior to UNet in terms of accuracy by 4.7%, with Unet being ranked second in terms of its performance. However, since we applied the extension of the Description domain by software, the final accuracy of YOLOv4-tiny is considered to be close to 100%.  
The final accuracy of this model is 95.22%, which exceeds the accuracy of other models by 20-22% [36]. At the same time, the model we used enables recognizing and subsequently analyzing text in 80 languages [37].  
In the course of our research, we faced the problem of a small dataset caused by \"manual\" assembly of said dataset. However, we are currently working on a solution to this problem. It can be expanded by saving the images received by the server and the coordinates of the received segments if they are successfully found. In the future, this will enable additional training of neural networks and improvement in recognition accuracy. Additionally, we expect to study the binarization of the obtained segments, which can improve the text recognition accuracy.  
Further work will aim to add the necessary functionalities for data comparison, i.e., interfacing with databases. Additionally, we expect to configure and use GPUs for computations performed by neural networks, thus significantly reducing the processing time of requests by the server.  
Additionally, we are considering the possibility of transferring neural network computations to mobile devices to reduce the entire system cost of ownership.  
As part of further evolution of our implementation, we plan to test it by integrating it into already existing product management software. This will enable the collection of field operation statistics as well as feedback from immediate users. Any shortcomings identified and the introduction of new functionalities will be managed based on the immediate user feedback.