# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 7 Challenges and Opportunities
Even when comparing identical products, the orientation, illumination, reflection, resolution and other aspects of the shelf image may differ from those of the reference image; the training data environment does not match the deployment data environment,and the model's performance decreases naturally. The domain shift issue can also be minimised with a GAN algorithm since it is trained to learn how to convert iconic samples into apparent in-store images. Then, the model, trained over more data, is able to extract rich invariant features. For cross-domain detection and recognition, transfer learning is commonly employed, with a vast majority of frameworks using a model pre-trained on ImageNet, such as [4; 44; 90; 98]. In [90], after training the network on ImageNet, the model was fine-tuned on the Grocery Store dataset [46] for the domain adaption. [PERSON] et al. [87] design a CNN model that combines two technical modules. The adversarial module handles the domain shift by gradually reducing the disparity between different domains and the self-attention module captures discriminative image features that are essential for fine-grained recognition. Other domain generalization techniques should be researched, including the possibility of adapting strategies defined for other application scenarios [161]. In the presence of noise and perturbations, Deep Convolutional Neural Networks and transformer-based architectures are two effective solutions, with the latter requiring even more sufficient data [162]. A robust model is constructed at the cost of heavy frameworks that are computationally expensive. These characteristics may be unfeasible in the implementation of real-time models on edge devices, but techniques for image pre-processing can be used to enhance model performance. Finding a fair balance between accuracy and efficiency is crucial, though. For example, [PERSON] et al. [98] avoid template-matching-based object detection because it is time-consuming; it selected a light backbone, the ResNet-18, and extracted multi-scale features to improve the product location. Product label analysis is a particular case of scene text recognition. Recent studies focused on providing an efficient solution to STR in tough environments, such as texts with arbitrary shape and orientation, various scales and text fonts, blur, distortion, occlusion, multilingual texts or even with a complex background. In fact, in a grocery environment, all these challenging tasks may occur. Independently of the deep learning model's architecture, a considerable amount of adequate data is crucial to train and assess the model. Given the scarcity of high-quality samples and annotations, creating a synthetic dataset based on the limited data available is a possible strategy. For example, in [163], the authors suggested an efficient framework to generate realistic images via a 3D graphics engine. Subsequently, the realistic and real-world sample distribution between training and testing should be evaluated. Another path could be the implementation of the GAN algorithm. [PERSON] et al. [164] separated the text instance from the wild background benefiting from a generative adversarial architecture. Moreover, the design of effective data augmentation approaches could be a sustainable solution to overcome the accessible data problem. On the other hand, developing unsupervised algorithms, such as [165; 166], can be further studied to take advantage of unlabelled real-world data. The scarcity of appropriate datasets is not solely a result of the lack of images taken in-store, but also due to packages written in different languages. Text recognition algorithms are frequently trained to identify English texts, and performance in other languages is critical. The first steps are being taken in the creation of multi-lingual datasets and, once again, synthetic multi-lingual datasets are an interesting solution to overcome this limitation. The performance of a text recogniser can be affected by complex text images.