Scene text detection is one of the most challenging problems in computer vision and has attracted great interest. Different from generic object detection, scene text detection mainly suffers from the large variance of scale, aspect ratio, and orientation in scene text. In this paper, we propose an effective and efficient model (SEG-FPN) for scene text detection, which is based on Feature Pyramid Network (FPN) and Linking Segments (SegLink). We incorporate feature pyramid mechanism with Single Shot Detector (SSD) framework to deal with different scale texts, and link locally detectable elements to detect texts of different orientations and aspect ratios. Moreover, compared with SSD, we enlarge the feature map of deep layers to better localize the large texts and recognize the small texts accurately. Experiments on ICDAR2015 and ICDAR2013 datasets demonstrate that our method can achieve comparable performance in terms of both accuracy and time. Specifically, SEG-FPN achieves an f-measure of 0.820 at 10.3 fps for 1280*768 ICDAR 2015 Incidental text images, and an f-measure of 0.879 at 19.2 fps for 512*512 ICDAR 2013 focused scene text images.

Chinese scene text reading is one of the most challenging problems in computer vision and has attracted great interest. Different from English text, Chinese has more than 6000 commonly used characters and Chinese characters can be arranged in various layouts with numerous fonts. The Chinese signboards in street view are a good choice for Chinese scene text images since they have different backgrounds, fonts and layouts. We organized a competition called ICDAR2019-ReCTS, which mainly focuses on reading Chinese text on signboard. This report presents the final results of the competition. A large-scale dataset of 25,000 annotated signboard images, in which all the text lines and characters are annotated with locations and transcriptions, were released. Four tasks, namely character recognition, text line recognition, text line detection and end-to-end recognition were set up. Besides, considering the Chinese text ambiguity issue, we proposed a multi ground truth (multi-GT) evaluation method to make evaluation fairer. The competition started on March 1, 2019 and ended on April 30, 2019. 262 submissions from 46 teams are received. Most of the participants come from universities, research institutes, and tech companies in China. There are also some participants from the United States, Australia, Singapore, and Korea. 21 teams submit results for Task 1, 23 teams submit results for Task 2, 24 teams submit results for Task 3, and 13 teams submit results for Task 4. The official website for the competition is http://rrc.cvc.uab.es/?ch=12.