# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 4 Related Datasets
### Datasets for Grocery Product Detection and Recognition
It contains images of German products (such as fruits, vegetables, cereal packets, pasta and bottles) from 60 categories placed on a table to simulate the real environment. The training set includes 4380 images that involve one or more products of a single class with a clean background. There are 16,620 test images with a more complex background, showing single or multiple objects of different classes. The overlap of some objects causes partial occlusion and each scene is acquired with three different lighting conditions. The annotations include label pixels for semantic segmentation methods and bounding box coordinates for bounding box detection. The supercategory and subcategory are also annotated. **Unitail-Det**[50] aims to support level-aligned grocery product detection on shelves. A total of 11,744 quality images of shelves in supermarkets were captured, achieving 1,740,037 quadrilateral annotations. Quadrilateral bounding boxes are more accurate in covering objects. Additionally, another set of 500 images of 3024 \(\times\) 4032 resolution is provided with another domain. The number of quadrilateral instances annotated is 37,071. There is a large diversity of products, including the most common grocery, delicatessen, textiles, electronics, and household products and medicines, among others. **Unitail-OCR**[50] was created to sustain retail product recognition through product matching via robust reading. In the gallery of products to be recognised, there are 1454 fine-grained products with frontal photos. Among these products, there are 10,709 labelled text regions located and 7565 legible word transcriptions. For testing purposes, 10k images of products were cropped, as well as the text on the packaging. In total, 18,972 text regions were detected, but only 13,416 are legible and have been transcribed for text recognition. A vocabulary list is provided with all words presented. The characteristics of this dataset meet the existing needs in terms of detection and text recognition of supermarket products. **RPC dataset**[5] consists of 83,739 images of Chinese products; 53,739 single-product exemplary images for training; 30,000 checkout images for validation and testing. Each training image is captured in controlled conditions with four cameras from different views. Designed for automatic checkout applications, the test images contain several products placed on a table. The images are grouped into three degrees of clutter, according to the number of items presented. A hierarchical structure annotation of 200 fine-grained product categories can be coarsely categorised as 17 meta-classes. Essentially designed for the automatic checkout scenario, the dataset can be used for detection and counting tasks. **CAPG Grocery Product Dataset**[44] contains 102 grocery products of 3 types of products belonging to 5 different brands: box-like packaged products (69 classes); bag-packaged products (15 classes); tube-packaged products (18 classes). For each class, a photo is taken under controlled conditions as the training set. Within each class, there can be sub-classes representing different sizes or flavours of that product; therefore, the variance of the visual features among sub-classes is small. The dataset is adequate for fine-grained grocery product recognition, being present in 177 sub-classes. As the testing set, 234 images of products on racks from 2 Chinese stores were collected. **RP2K**[51] is a large dataset that contains 2395 unique SKUs of the Chinese market that can be grouped into 7 coarse categories. In that sense, products of different sizes, flavours/types are classified as another class. Like the previous dataset, it was created for fine-grained image classification, but with substantial annotations that include class, category, brand, flavour/type, size and shape. Since the real environment is also a concern, 384,311 cropped images of retail products on the shelves were extracted in physical supermarkets.