  Scene text detection has witnessed rapid progress especially with the recent
development of convolutional neural networks. However, there still exists two
challenges which prevent the algorithm into industry applications. On the one
hand, most of the state-of-art algorithms require quadrangle bounding box which
is in-accurate to locate the texts with arbitrary shape. On the other hand, two
text instances which are close to each other may lead to a false detection
which covers both instances. Traditionally, the segmentation-based approach can
relieve the first problem but usually fail to solve the second challenge. To
address these two challenges, in this paper, we propose a novel Progressive
Scale Expansion Network (PSENet), which can precisely detect text instances
with arbitrary shapes. More specifically, PSENet generates the different scale
of kernels for each text instance, and gradually expands the minimal scale
kernel to the text instance with the complete shape. Due to the fact that there
are large geometrical margins among the minimal scale kernels, our method is
effective to split the close text instances, making it easier to use
segmentation-based methods to detect arbitrary-shaped text instances. Extensive
experiments on CTW1500, Total-Text, ICDAR 2015 and ICDAR 2017 MLT validate the
effectiveness of PSENet. Notably, on CTW1500, a dataset full of long curve
texts, PSENet achieves a F-measure of 74.3% at 27 FPS, and our best F-measure
(82.2%) outperforms state-of-art algorithms by 6.6%. The code will be released
in the future.
