# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 1 Introduction
Furthermore, new products are launched frequently and their appearance is likely to change over time to attract consumers' attention; also, due to marketing strategies, the same product may present slight variations in the packaging (e.g., during a promotional campaign) or the items may be integrated to be sold together. Figures 1 and 2 represent these common challenges for fine-grained classification. Deep learning and Convolution Neural Networks (CNN) have been shown to be successful in object recognition, much like in many other application scenarios. Different levels of automatically learned features have proven to be more accurate and discriminatory than manually extracted features. Although deep learning methods are extensively used, there are two main drawbacks: (1) the performance is impaired by small training sets and (2) when the model is trained to learn new classes/tasks the previously learned classes/tasks are not preserved. In the grocery context, retraining the complete network with all classes when a new product appears in the market may be unfeasible since even the smallest supermarket has thousands of products available [3]. The wide variety of articles is, on the other hand, a constraint in creating an adequate dataset. Deep neural networks outperform traditional detection and recognition techniques, but they are often bottlenecked by limited datasets. The amount of labelled data is as important as the quality. Ideally, the images of each product should be taken in a real environment, from different perspectives and lighting conditions, in multiple retail stores. Manually annotating the object's location and providing, in detail, the attributes of each product (e.g., brand, flavour, type, size, etc.) are expensive tasks. It becomes even more cumbersome when all the textual information on the package is to be provided. For all these reasons, databases are limited and under-represented. Some researchers try to propose adaptable frameworks, and several systems introduce data augmentation during the training, e.g., models [4; 5] generate synthetic context with the generative adversarial network (GAN) [6], some resort to transfer learning [7] and others [8; 9] used a reference database with images of products taken under a controlled environment from several points of view for the training phase to mitigate this gap. The match classification approach is also convenient to handle new grocery products. Even after the minimisation of this issue, there are still challenges with the object's detection and identification, given the domain shift, complex background, image quality, intra-class similarity and limitations of computational resources. For recognizing grocery products, visual features are no longer the only option. Today, textual information--or a combination of the two--is already commonplace. Regardless of the techniques that each method chooses to overcome the different challenges, the system should assist the buyer throughout the process of selecting and taking the product. In other words, it should detect and recognise the products displayed on the racks, based on images or videos captured by the user, and guide him to acquire the correct product. After determining the product category, the client should be provided with more information about subcategories, such as sub-brands, flavours, types and quantities. All this relevant information is available on the label, although sometimes not in the best way. Therefore, in the next stage, the system should detect the label and identify the regions of interest (RoI), which could be logos, alphanumeric characters, words and symbols. The extracted RoI is to be processed to translate useful information. Finally, the classifier identifies the product based on text features or recognised texts.