Care Label Recognition The paper introduces the problem of care label recognition and presents a method addressing it. A care label, also called a care tag, is a small piece of cloth or paper attached to a garment providing instructions for its maintenance and information about e.g. the material and size. The informationand instructions are written as symbols or plain text. Care label recognition is a challenging text and pictogram recognition problem  the often sewn text is small, looking as if printed using a nonstandard font; the contrast of the text gradually fades, making OCR progressively more difficult. On the other hand, the information provided is typically redundant and thus it facilitates semisupervised learning. The presented care label recognition method is based on the recently published EndtoEnd Method for MultiLanguageScene Text, E2EMLT, Busta et al. 2018, exploiting specific constraints, e.g. a care label vocabulary with multilanguage equivalences. Experiments conducted on a newlycreated dataset of 63 care label images show that even when exploiting problemspecific constraints, a stateoftheart scene text detection and recognition method achieve precision and recall slightly above 0.6, confirming the challenging nature of the problem.

The Equipment Nameplate Dataset for Scene Text Detection and Recognition In this paper, we introduce the Equipment Nameplate Dataset, a large dataset for scene text detection and recognition. Natural images in this dataset are taken in the wild and thus this dataset includes various intraclass inconsistency such as ill illumination conditions and partly occluded, which makes our dataset more challenging than other datasets. In order to make people train detection and recognition model separately, we annotate our dataset not only word instance, but also text region by using rectangle bounding boxes. Some detailed statistics information about our dataset will be given so that people can use them to analyse and develop their own models. Moreover, we use our dataset to test some famous detection and recognition models and present the corresponding results in order to make researcher compare them with their own models. Dataset will be publicly available on the website.