  Though deep learning based scene text detection has achieved great progress,
well-trained detectors suffer from severe performance degradation for different
domains. In general, a tremendous amount of data is indispensable to train the
detector in the target domain. However, data collection and annotation are
expensive and time-consuming. To address this problem, we propose a
self-training framework to automatically mine hard examples with pseudo-labels
from unannotated videos or images. To reduce the noise of hard examples, a
novel text mining module is implemented based on the fusion of detection and
tracking results. Then, an image-to-video generation method is designed for the
tasks that videos are unavailable and only images can be used. Experimental
results on standard benchmarks, including ICDAR2015, MSRA-TD500, ICDAR2017 MLT,
demonstrate the effectiveness of our self-training method. The simple Mask
R-CNN adapted with self-training and fine-tuned on real data can achieve
comparable or even superior results with the state-of-the-art methods.
