# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 4 Related Datasets
### Scene Text Recognition Datasets  
Less attention has been given to the processing of text in grocery or retail labels. Hence, there is a lack of information and specific datasets about product labels, forcing the text reader modules to be trained on scene text datasets, as shown in Figure 5. The benchmark datasets to support scene text recognition (STR) can be divided into three groups: synthetics, regular, and irregular datasets. Synthetic datasets were created because labelling and its verification are very time-consuming. Artificial distortions of the image are often applied to obtain a higher quantity of realistic images and, automatically, the annotation of object localization follows the same distortion. Next, the most relevant synthetic datasets are described. **MJsynth**[57] is a large-scale dataset for horizontal text recognition that contains 8.9 M English word box images. The process of generating synthetic data is the following: (1) font rendering; (2) border and shadow rendering; (3) base colouring; (4) projective distortions; (5) natural data blending; (6) noise. The generated word samples have a fixed height of 32 pixels and a variable width according to the length of the word. **SynthText**[58] contains 800,000 generated scene images that contain several horizontal text instances displayed on multi-perspectives. The text samples used to create these images were extracted from an English text dataset that includes nouns, numbers, symbols and punctuation marks. Initially, data were designed for scene text detection by labelled text region location, but with labelled extracted text regions, SynthText has also been used for scene text recognition. Label information is rich, annotated at the text-strings-, word- and character-level bounding boxes. The data have more than 7 M cropped word boxes and almost 30 M characters. \begin{table}
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**Datasets** & **\#Images** & **\#Classes** & **Objective** & **Labels** & **Language** & **Resolution** \\ \hline Products-6K [8] & 12,917 R & 638 R & Classify products on the shelf or 373 Q & 104 Q & & SKU classes, image class & Creek & 80  
Regular STR datasets collect several text images of the real world that are relatively easy to detect and recognise. Some images are noisy, with variations in illumination and scale or low resolution, but all text instances have horizontal orientation and are easily separated. **IIIT5K-Word**[63] contains 5000 cropped words extracted from images crawled from Google Image Search. Some examples of query words used in the search engine are \"billboards\", \"signboard\", \"house numbers\", \"house name plates\" and \"movie posters\". The images are split into 2000 words for training and 3000 for testing. For each photo, two lexicon lists are provided: a 50-word lexicon and a 1000-word lexicon. IIIIT5K-Word is labelled at the word and character level. **Street View Text**[64] is a dataset that contains 350 images downloaded from Google Street View, searched by English business names. Hence, the most frequent words belong to business signage. The dataset was designed for text detection and word recognition, but all words in the photo are not referenced in the ground-truth label. A lexicon list is given for each image and the aim is to find words entered in the lexicon, which may correspond to the scenario where a blind person searches for supermarket products with a grocery list which is the lexicon. In selecting images, the focus was on obtaining frontal texts by minimizing the skew angles. Some of the images are noisy, blurry and of low-resolution. The median height of the images is 55 pixels, but image quality varies greatly. From 350 images, 101 are for training and 249 for testing. There are 725 words labelled: 211 for the training set and 514 for the test set. **ICDAR2003**[65] was created for the ICDAR 2003 Robust Reading competitions to support the reading of texts of natural scenes.