Automatic deep learning computer vision based retail store checkout system

Abstract

A retail store automated checkout system uses images, video, or depth data to recognize products being purchased to expedite the checkout process and improve accuracy. All of a store's products, including those not sold in packages, such as fruits and vegetables, are imaged from a series of different angles and in different lighting conditions, to produce a library of images for each product. This library is used in a checkout system that takes images, video, and depth sensor readings as the products pass through the checkout area and remove bottlenecks later in the checkout process. Recognition of product identifiers or attributes such as barcode, QR code or other symbols, as well as OCR of product names, as well as the size and material of the product, can be additional or supplemental devices for identifying products being purchased. In another embodiment, an existing checkout or self-checkout scanner is enhanced with image recognition of products to achieve the same effect.