  In this paper, we introduce the ShopSign dataset, which is a newly developed
natural scene text dataset of Chinese shop signs in street views. Although a
few scene text datasets are already publicly available (e.g. ICDAR2015,
COCO-Text), there are few images in these datasets that contain Chinese
texts/characters. Hence, we collect and annotate the ShopSign dataset to
advance research in Chinese scene text detection and recognition.
  The new dataset has three distinctive characteristics: (1) large-scale: it
contains 25,362 Chinese shop sign images, with a total number of 196,010
text-lines. (2) diversity: the images in ShopSign were captured in different
scenes, from downtown to developing regions, using more than 50 different
mobile phones. (3) difficulty: the dataset is very sparse and imbalanced. It
also includes five categories of hard images (mirror, wooden, deformed, exposed
and obscure). To illustrate the challenges in ShopSign, we run baseline
experiments using state-of-the-art scene text detection methods (including
CTPN, TextBoxes++ and EAST), and cross-dataset validation to compare their
corresponding performance on the related datasets such as CTW, RCTW and ICPR
2018 MTWI challenge dataset.
  The sample images and detailed descriptions of our ShopSign dataset are
publicly available at: https://github.com/chongshengzhang/shopsign.
