  Unifying text detection and text recognition in an end-to-end training
fashion has become a new trend for reading text in the wild, as these two tasks
are highly relevant and complementary. In this paper, we investigate the
problem of scene text spotting, which aims at simultaneous text detection and
recognition in natural images. An end-to-end trainable neural network named as
Mask TextSpotter is presented. Different from the previous text spotters that
follow the pipeline consisting of a proposal generation network and a
sequence-to-sequence recognition network, Mask TextSpotter enjoys a simple and
smooth end-to-end learning procedure, in which both detection and recognition
can be achieved directly from two-dimensional space via semantic segmentation.
Further, a spatial attention module is proposed to enhance the performance and
universality. Benefiting from the proposed two-dimensional representation on
both detection and recognition, it easily handles text instances of irregular
shapes, for instance, curved text. We evaluate it on four English datasets and
one multi-language dataset, achieving consistently superior performance over
state-of-the-art methods in both detection and end-to-end text recognition
tasks. Moreover, we further investigate the recognition module of our method
separately, which significantly outperforms state-of-the-art methods on both
regular and irregular text datasets for scene text recognition.
