# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 4 Related Datasets
### Scene Text Recognition Datasets
It contains 509 images, 258 for training and 251 for testing. Bounding boxes surround text instances, and ground-truth text location is given. For the word recognition task, there are 1156 word images in the training set and 1110 images for evaluation. Excluding words less than three characters and ones that contain non-alphanumeric characters, the result is 867 text regions. Character recognition is also possible by ground-truth character labels; there are 6185 characters for training and 5430 for testing. **ICDAR2013**[66] had three challenges. For reading text in Born-Digital Images (Web and E-mail), 561 images were collected with a minimum size of 100 \(\times\) 100 pixels, with 420 used for training and 141 for evaluation. The dataset is labelled for text location, text segmentation and word recognition. For the last task, words with fewer than three characters were excluded, resulting in 3564 cropped words in the training set and 1439 in the test set. **Char74k**[67] collects 1922 images of sign boards, hoardings and advertisements taken in the streets of India. Some photos were taken of products in supermarkets and shops, with English and Kannada texts present in this dataset. Focusing on the English dataset, 12,503 characters were annotated and 4798 were labelled as bad images due to excessive occlusion, low resolution or noise. In character classification, they distinguish upper and lower cases, which makes a total of 62 classes with the inclusion of digits. They also created a complementary English dataset with 3410 hand-printed characters generated by  
Figure 5: Image examples of publicly available datasets for scene text recognition. (**a**) ICDAR19-LSVT [59]. (**b**) MSRA-TD500 [60]. (**c**) CUTE80 [61]. (**d**) SCUT-CTW1500 [62]. 55 volunteers. A third English dataset was synthetically generated by 254 different fonts in 4 styles (normal, bold, italic and bold+italic), reaching 62,922 characters. As labelled grocery products have texts of countless positions, shapes and densities, the aforementioned datasets are unsuitable for the scope of this paper. On the other hand, irregular datasets cover complicated text scenes, such as curved texts, with different perspectives, orientations and positions. **ICDAR 2015**[68] is a dataset addressed to Challenge 4 of the ICDAR2015 Robust Reading Competition. About 1500 images were acquired with Google Glasses without paying attention to the position, perspective or quality. As a result, the dataset includes blurry, noisy and low-quality images. As in real scenarios, texts may have different styles, arbitrary shapes and orientations, and even be curved and illegible. The dataset was designed for text localization and word recognition. A total of 4468 training instances were labelled with a quadrilateral bounding box, and its transcription is provided when the word is readable. Otherwise, the illegible word is annotated as \"###\". For testing the model, the dataset contains 2077 text images..  
**MSRA-TD500**[60] consists of 500 images captured by a pocket camera in indoor and outdoor scenes. Each image contains Chinese, English or a combination of both languages. Texts have different sizes, fonts, directions and colours. The primary application of this dataset is multidirectional text detection, where text lines are confined by rotatable rectangles. **SVT Perspective**[69] is a dataset based on the StreetViewText dataset, which consists of collecting photos from Google Street View, using English business names as a means of searching. SVT Perspective contains images of the same addresses, but the selection focus on non-frontal texts, choosing texts viewed from different perspectives and of arbitrary orientations, summing to 238 images. They preserved the same lexicon of SVT and labelled only words presented in the specific lexicon for that image. In total, 639 words were annotated using quadrilaterals. The heights of cropped words vary from 9 to 330 pixels. **CUTE80**[61] consists of 80 curved text images for natural scene image recognition.