  Text detection/localization, as an important task in computer vision, has
witnessed substantialadvancements in methodology and performance with
convolutional neural networks. However, the vastmajority of popular methods use
rectangles or quadrangles to describe text regions. These representationshave
inherent drawbacks, especially relating to dense adjacent text and loose
regional text boundaries,which usually cause difficulty detecting arbitrarily
shaped text. In this paper, we propose a novel text regionrepresentation
method, with a robust pipeline, which can precisely detect dense adjacent text
instances witharbitrary shapes. We consider a text instance to be composed of
an adaptive central text region mask anda corresponding expanding ratio between
the central text region and the full text region. More specifically,our
pipeline generates adaptive central text regions and corresponding expanding
ratios with a proposedtraining strategy, followed by a new proposed
post-processing algorithm which expands central text regionsto the complete
text instance with the corresponding expanding ratios. We demonstrated that our
new textregion representation is effective, and that the pipeline can precisely
detect closely adjacent text instances ofarbitrary shapes. Experimental results
on common datasets demonstrate superior performance o
