  Arbitrary-shaped text detection has recently attracted increasing interests
and witnessed rapid development with the popularity of deep learning
algorithms. Nevertheless, existing approaches often obtain inaccurate detection
results, mainly due to the relatively weak ability to utilize context
information and the inappropriate choice of offset references. This paper
presents a novel text instance expression which integrates both foreground and
background information into the pipeline, and naturally uses the pixels near
text boundaries as the offset starts. Besides, a corresponding post-processing
algorithm is also designed to sequentially combine the four prediction results
and reconstruct the text instance accurately. We evaluate our method on several
challenging scene text benchmarks, including both curved and multi-oriented
text datasets. Experimental results demonstrate that the proposed approach
obtains superior or competitive performance compared to other state-of-the-art
methods, e.g., 83.4% F-score for Total-Text, 82.4% F-score for MSRA-TD500, etc.
