# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 7 Challenges and Opportunities
Hence, in addition to being robust, the recognition model must also be flexible enough to adapt or retrain itself with less effort when a new product or packaging appears. Training a CNN with just new classes is not feasible because it cannot preserve the past learned classes; on the other hand, the frequent retraining of the complete network with all classes is impractical, given the existence of thousands of products. To handle this issue, incremental learning or one-shot learning are two possible approaches. Within incremental learning methods, there is the fine-tuning approach, which consists of using the pre-trained network and adjusting the old parameters to adapt to the new data. However, this technique risks learning new classes while forgetting the old ones. In the retraining process, freezing some layers and setting up adequate parameters, such as a small learning rate, are important strategies to keep the old knowledge. Model configuration is a tricky task and becomes even more challenging with the constantly growing number of classes. Recently, knowledge distillation has been explored to handle the class incremental problem [158, 159, 160]. The foundation of one-shot learning for computer vision tasks is Siamese neural networks (SNN); these special types of CNNs are trained to evaluate the distance between features in two input images. SNNs have the enormous benefit of not requiring intensive retraining to identify new classes after having been trained on large datasets. Regarding speed and accuracy, the SNNs may outperform other types of methods. Nevertheless, in terms of memory and computational resources, they are expensive since they need to train two models. [PERSON] et al. [84] proposed a one-shot object detector, and [PERSON] et al. [92] classified grocery products with Siamese neural networks. This topic might attract increasing interest when additional datasets are released. The huge diversity of grocery items poses a challenge to developing a suitable dataset. Although deep networks perform better than conventional detection and recognition methods, they are sometimes constrained by scarcity and under-represented data. Collecting grocery images captured in the real environment and subsequently annotating the dataset are laborious tasks. Therefore, most available datasets represent a small number of classes and/or contain restricted images per class. This limitation damages the efficiency of the model and creates obstacles to being expanded to real-world applications. In that sense, to increase the size of the training set, some frameworks apply data augmentation [78, 90, 98], and other researchers suggest generating realistic samples by GAN to achieve reliable results [5, 97]. Another strategy widely used to minimise the problem of the lack of adequate datasets is the compilation of grocery product photos taken under a controlled environment, occasionally from a range of perspectives, that act as a reference of the class. Almost all datasets designed for image identification used this approach and, as a result, most retail recognition methods rely on template-matching-based techniques. This technique involves the interpretation of all positions of the template and measuring how well the template and the query image match at each location. The process is repeated for each template, which is very time-consuming. Instead of a feature extraction match, the comparison can be made with feature vectors of two convolutional nets using SNNs. They also have high computational costs. In that sense, further investigations are required in order to develop an accurate and efficient model that only needs a single image to classify. Whether the system uses template-matching-based techniques or Siamese networks, it will likely be challenging to correctly identify the class due to minor differences in the same product.