# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 6 Product Label Analysis  
Various techniques can be used to identify grocery products. There are systems based on barcode readers, visual appearance and/or text labels. The label provides information that can be paramount to classify the product or to afford detailed product characteristics. Consequently, some researchers have already started to use textual information in their product classification models. In [99], employing optical character recognition (OCR) techniques, a histogram of words for each class is built from training images, which provides the frequency of words in a given class. This word histogram measures the confidence of the product's name introduced by the user in a corresponding class and is used to rank the possible classes. However, the extracted text information is only used to identify the coarse class of each product of a shopping list. The fine-grained product classification is based on clusters of mid-level discriminative patches. Different model versions were tested in edge devices, although the accuracy was always less than 62%. Previous methods applied visual and text features independently. Nevertheless, product identification, especially those with similar appearance, can benefit from textual information. The experiments conducted by [PERSON] et al. [9] support the idea that combining visual features and text descriptions improves the classification model's performance, especially in distinguishing between visually similar items. If for any constraint only one type of feature can be employed, models using visual features achieved better results. They also concluded that introducing features of the referenced images is essential to achieve higher classification performance since visual information is clearer. For image grocery product classification, they used a multi-view generative model, Variational Canonical Correlation Analysis (VCCA) learns a shared latent space for four views of natural and referenced images and product descriptions. Referenced images help to separate the items based on their colour and shape, whereas text information joins the groceries based on their ingredients and flavour. Despite the good results achieved, its implementation on a mobile device has not been tested. A different approach was presented in [8], proposing the fusion of visual and textual descriptors. Google Cloud Vision's OCR mechanism extracted textual information of reference and query images. The former information was concatenated with the product description provided in the dataset. According to a distance metric between the two sets of words, all index images are ranked in descending order, and then the list is re-ranking by the similarity between visual information. The use of inverse order of descriptors is also suggested. The limitations of the model were related to the differences between referenced and query images. Usually, the referenced images only contain the front of the package. If the shopper takes a photo of another side, the system has difficulty identifying the item correctly. There is also the possibility of having a database of side views of the product and the system assigns a class that belongs to the same brand or category, but not the correct SKU because the minor details are frequently on the frontal view. Even when the natural photo captures the frontal view, the layout may differ from the referenced image because of marketing strategies. The system proposed in [9] can handle these obstacles better because the latent representations are extracted from multi-view encoders. Nevertheless, when the model is implemented with only frontal view packages, the model presented by [PERSON] et al. [8] has better performance. In [50], the authors resort to a