# Neural Network-Based Price Tag Data Analysis
## 1 Introduction  
Today people frequently face incorrect or irrelevant information on price tags in stores, with a large number of products resulting in a decrease in customer's loyalty. This therefore leads to losses of profits and reputation.  
In each store, the product information can be updated every 7 days, and depending the quantity of products, the price tags verification can take various amounts of time but no less than several hours. Store employees usually do this before the store opens, but they cannot always accomplish this task on time. Additionally, checking can take place after the end of the shift, which leads to overtime work.  
The most important information on price tags to keep track of is usually the price, product name and product specifications. This specific information is subject to the most frequent change and requires continuous verification. Pictures of price tags can also contain a lot of additional information that is not relevant to this study, such as a fragment of the shop counter, fragments of adjacent products and variable text falling outside the areas of interest, and segmentation can be used to get rid of this information. This approach allows us to leave only those parts of the image containing relevant text only.  
In many countries, the information on price tags is a public offer, for instance, in Russia, the Philippines and China, which means that incorrect price tag data can result in the store receiving a penalty [1; 2; 3] or incurring reputational losses.  
Electronic shelf labels are a potential solution to the price tag data verification problem, but at present they are widespread and most stores are still working with classic paper price tags. Checking price tags can be carried out both without using any special equipment, which takes a lot of time, and using, for example, portable data collection terminals (PDCT). Although modern technologies allow the time spent on this activity to be reduced, they do not completely eliminate the chance of errors occurring during price tag verification due to the fact that PDCTs scan barcodes only while the price tag data analysis is done by a store employee. Moreover, the use of special equipment often entails a huge equipment cost and the software to operate it.  
While using PDCT, the employee needs to manually verify the data on the price tag and in the database, which can lead to human error. In our approach, all information on the price tag is analyzed, and the employee will immediately see the result of comparing the information on the price tag with the information in the database. In this way, the errors due to the human factor can be eliminated through the use of neural networks, making the data verification process fully automatic and thus minimizing the number of errors and handing the verification process over from costly PDCTs to more affordable mobile devices, as well as expediting the price tag data verification process.  
The task of price tag verification is non-trivial, which complicates it's solution in the conventional ways. To solve such problems, various algorithms and smart systems are used, for example, KNN [4] and NSGA-III [5]. However, in the modern world neural networks can be used to solve such problems, there are a lot of projects that use neural networks for different tasks: speaker verification [6], speech rehabilitation after speech organs surgical interventions [7], authorship identification [8], water level estimation in sewer pipes [9], determining the presence of lung cancer or diabetes in patients on the basis of symptoms [10; 11], etc.