A TeacherStudent Learning Based BornAgain Training Approach to Improving Scene Text Detection Accuracy With the recent success of convolutional neural network (CNN) based text detection approaches, designing better CNNbased text detection frameworks has become a major research focus to improve text detection accuracy. In this paper, instead of following this direction, we propose to use a bornagain training strategy, which is based on teacherstudent learning (TSL), to improve the accuracy of the stateoftheart CNNbased text detectors. More specifically, given a welltrained CNNbased text detector, we take it as a teacher model and train from scratch a new student model with the same topology under the supervision of both the teacher model and groundtruth labels. Furthermore, we propose a new proposalfree multilevel feature mimicking approach to making multilevel convolutional feature maps be effectively mimicked in a unified manner. Experiments demonstrate that the student models trained by the proposed approach can achieve substantially better results than their teacher models and have better generalization abilities.

A TextContextAware CNN Network for Multioriented and Multilanguage Scene Text Detection The existing deep learning based stateoftheart scene text detection methods treat scene texts a type of general objects, or segment text regions directly. The latter category achieves remarkable detection results on arbitraryorientation and large aspect ratios of scene texts based on instance segmentation algorithms. However, due to the lack of context information with consideration of scene text unique characteristics, directly applying instance segmentation to text detection task is prone to result in low accuracy, especially producing false positive detection results. To ease this problem, we propose a novel textcontextaware scene text detection CNN structure, which appropriately encodes channel and spatial attention information to construct contextaware and discriminative feature map for multioriented and multilanguage text detection tasks. With high representation ability of textcontextaware feature map, the proposed instance segmentation based method can not only robustly detect multioriented and multilanguage text from natural scene images, but also produce better text detection results by greatly reducing false positives. Experiments on ICDAR2015 and ICDAR2017MLT datasets show that the proposed method has achieved superior performances in precision, recall and Fmeasure than most of the existing studies.