# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 1 Introduction  
The retail industry has been integrating technological innovations throughout its product chain in order to reduce costs and, cumulatively, improve customer experience, since increasing profit margins and attracting customers are the primary goals of any entrepreneur. In the retail context, the automatic detection and recognition of products has allowed a more efficient use of resources and revolutionised the way customers buy. They want the purchase process to be simple and fast; therefore, they value payout and tools that allow them to find desired items, know the product's availability and avoid payment queues. Automatic self-checkout systems are the solution to fill the last need. Real-time inventory management, in particular automatic shelf monitoring, is also possible with computer vision tools, with out-of-stock shelves being detected in real-time by capturing images of racks. By the same philosophy, there is an opportunity to verify if the product displays and store layout follow the plan known as a planogram. Self-service technology isbeing applied to create autonomous stores and to permit a self-guided shopping experience, and product recognition devices can add value to the customer experience by assisting them in the correct purchase of the product. In a grocery environment, food and nutrition labelling are extremely important, as the information allows consumers to make more conscious food choices, appropriate to their needs and preferences, contributing to the correct storage, preparation and consumption of food. Although interpreting grocery labels may not be an easy task, either due to a lack of food literacy of consumers to understand some of the available information or due to other types of limitations, including visual clutter. This motivates the development of auxiliary tools, democratizing access to information and developing inclusive strategies; particularly, visually impaired people have strong limitations in carrying out daily activities, which include grocery shopping independently. Several studies have addressed this issue, focusing on the detection or recognition of specific products, often under unique conditions. Automatic grocery product detection and recognition in a real environment raise many challenges [1; 2], which are even more relevant in the case of images captured by visually impaired people as this disability prevents them from understanding if the image includes the product or if it is legible and readable. As a result, the items can appear in arbitrary poses and perspectives, cropped, with partial occlusions, with great differences of illumination, with reflection due to glossy product packages, at various distances from the camera, in a cluttered background, or in a blurred image due to camera shake, among others. This will have an impact on the performance of the implemented solution as the quality of the image is the basis for successful product recognition. When taking photos or filming grocery items, some methods apply image- or frame-processing strategies to select or improve image quality, such as multi-frame super-resolution techniques. Others frameworks use a system keep to warn the user when a product or label is detected. Beyond the problem of the quality of the image, recognition of similar logos and different products with identical appearances is challenging. Identifying subtle details is critical for a fine-grained product classification, since small variations in packaging are very common among products of the same category. Classification between a product's subcategory can be a difficult task, even for humans, for example, when identifying several flavours of cereals, differences in the quantity of shower gel, or the type of hair of the same shampoo brand. Intraclass product recognition systems must differentiate such minor variations in an uncontrolled environment.