  The prosperity of deep learning contributes to the rapid progress in scene
text detection. Among all the methods with convolutional networks,
segmentation-based ones have drawn extensive attention due to their superiority
in detecting text instances of arbitrary shapes and extreme aspect ratios.
However, the bottom-up methods are limited to the performance of their
segmentation models. In this paper, we propose DPTNet (Dual-Path Transformer
Network), a simple yet effective architecture to model the global and local
information for the scene text detection task. We further propose a parallel
design that integrates the convolutional network with a powerful self-attention
mechanism to provide complementary clues between the attention path and
convolutional path. Moreover, a bi-directional interaction module across the
two paths is developed to provide complementary clues in the channel and
spatial dimensions. We also upgrade the concentration operation by adding an
extra multi-head attention layer to it. Our DPTNet achieves state-of-the-art
results on the MSRA-TD500 dataset, and provides competitive results on other
standard benchmarks in terms of both detection accuracy and speed.
