  Most state-of-the-art scene text detection algorithms are deep learning based
methods that depend on bounding box regression and perform at least two kinds
of predictions: text/non-text classification and location regression.
Regression plays a key role in the acquisition of bounding boxes in these
methods, but it is not indispensable because text/non-text prediction can also
be considered as a kind of semantic segmentation that contains full location
information in itself. However, text instances in scene images often lie very
close to each other, making them very difficult to separate via semantic
segmentation. Therefore, instance segmentation is needed to address this
problem. In this paper, PixelLink, a novel scene text detection algorithm based
on instance segmentation, is proposed. Text instances are first segmented out
by linking pixels within the same instance together. Text bounding boxes are
then extracted directly from the segmentation result without location
regression. Experiments show that, compared with regression-based methods,
PixelLink can achieve better or comparable performance on several benchmarks,
while requiring many fewer training iterations and less training data.
