Retail supermarkets are an essential part of today's economy, and managing
them is a tedious task. One of the major problems faced by supermarkets today is to
keep track of the items available on the racks. Currently, the track of the product on the
shelf is kept by price tag readers, which work on a barcode detection methodology that
has to be customized for each store. On the other hand, if barcodes are not present on
the price tags, the data is manually fed by the staff of the store, which is really time-consuming. This paper presents a universal pipeline that is based on Optical Character
recognition and can be used across all kinds of price tags, and is not dependent on
barcodes or any particular type of price tag. This project uses various image-possessing
techniques to determine and crop the Area of Interest. It detects the price of the product
and the name of the product by filtering the OCR outputs based on the area and
dimensions of the bounding boxes of the text detected. Additionally, the presented
pipeline is also capable of capturing discounted prices, if any, for the products. It has
been tested over price tags of five different types, and the accuracy ranges from 78% to
94.5%.
Keywords: Artificial intelligence, Contour detection, Image processing, Optical character recognition, Retail supermarkets.
