# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 4 Related Datasets
### Scene Text Recognition Datasets
Therefore, images suffer complex backgrounds and variations in text scale, font, orientations and perspective. A set of polygon points for each curved text line labels 288 word instances. **COCO-Text dataset**[70] is based on the MS COCO dataset, which contains images of complex everyday scenes. The COCO-Text dataset contains non-text images and legible and illegible text images. It is the first large-scale dataset for text detection and recognition in natural scenes, including 63,686 images with 239,506 labelled text regions in the new version. For every word, the segmentation mask is annotated. Moreover, each word is categorised into three attributes: machine-printed vs. handwritten, legible vs. illegible, and English vs. non-English. **Total-Text**[71] dataset was intended to make a more significant contribution to the availability of curve-orientated texts, providing 1555 images that include horizontal, multi-oriented and curved texts. The dataset provides labels at word level by polygon bounding box. Ful annotations are provided for text detection, recognition and segmentation of 11,459 English text instances. **SCUT-CTW1500**[62] is a multi-lingual dataset that contains English and Chinese texts. At least one curved text appears in 1500 images from different web sources and texts have arbitrary shapes and orientations. In the last version of annotations, Chinese texts are labelled with \"###\" because their weight is not significant. Polygon bounding box coordinates are provided at the sentence level. **VinText**[72] is a dataset of Vietnamese scene images. The 2000 images contain text instances from several perspectives, sizes, orientations and shapes, providing about 56 k quadrilateral bounding boxes and word-level transcriptions. According to the authors, Vietnamese script is similar to Portuguese script, and the transfer learning technique might be interesting to Portuguese scenarios. **ICDAR 2017-MLT**[73] is a multi-lingual text dataset with 7200 training images, 1800 validation images and 9000 test images. The photos were extracted from various sourcesand represent a high diversity of domains, such as street views and pictures in microblogs. Variety is also presented in text format, including horizontal, multi-perspective and vertical texts. Chinese, Japanese, Korean, English, French, Arabic, Italian, German and Indian languages are equally represented in this dataset. For each language, there are at least 2000 images; however, each image may contain more than one language. Ground-truth quadrilateral bounding box is provided at the word level. Text recognition and script identification are possible because each recognizable word is associated with a script class and the transcription. There are 84,868 training word images and 97,619 test instances for these tasks. **ICDAR 2019--RRC-MLT**[73] is also a multi-lingual dataset that consists of 20,000 images of 10 different languages, where for each language 2000 photos were selected. In addition to the nine languages mentioned, Bengali is now presented. The training set contains 10,000 images with bounding box coordinates and another 10,000 images compose the test set. For the text recognition task, 89,177 cropped text instances are provided as training images and 102,462 as test images. ICDAR 2019 is not only distinguished from ICDAR 2017 by having added another language to the list but also by being more challenging. The weight of curved and vertical images is higher than in ICDAR 2019. They continue to have texts of different sizes, aspect ratios and directions. **ICDAR 2019--Art**[74] joins images of Total-Text, SCUT-CTW1500, curved texts of LSVT and 7111 newly collected images with high arbitrary shape, multi-oriented and curved text instances. The aim is to support models to be robust against diverse text formats and language variants, since there are Chinese and Latin texts. There are 10,166 images divided into 5603 training images and 4563 for evaluation. Text instances are annotated at the word level, enclosed by quadrilateral or polygon bounding boxes.