Vol. 8, Issue 2 (2019)

Author(s):
Neeraj Pratap

Abstract:
The problem of price discrepancies between physical stores and online retailers has been a growing concern for consumers and businesses alike. In this paper, we propose a real-time object detection system that utilizes image recognition and web scraping to identify and compare prices of physical items with prices of ecommerce sites. The motivation for this project is to provide consumers with a more efficient way to identify price discrepancies and take advantage of potential cost savings. In the literature review section, we examine existing research and methods for price comparison, including computer vision and machine learning techniques. We analyze the strengths and weaknesses of existing methods and highlight how the proposed approach differs. Our methodology section provides a detailed description of the tools and techniques used, including OpenCV, web scraping, and OCR. We also provide an overview of the steps involved in the process, including image processing, text recognition, and data analysis. The results section presents an analysis of the data collected, including a comparison of prices between physical stores and ecommerce sites. We discuss any patterns or trends that emerged from the data, including differences in pricing between different types of products or websites. Our system was able to successfully identify and compare prices in real- time, providing users with accurate and up-to-date information about price discrepancies. Overall, our proposed real-time object detection system shows promise in addressing the problem of price discrepancies between physical stores and online retailers. By utilizing image recognition and web scraping, we were able to provide consumers with a more efficient way to identify potential cost savings. Future research can focus on improving the accuracy of the system and expanding its scope to include additional features such as user reviews and product availability.