  Arbitrary text appearance poses a great challenge in scene text recognition
tasks. Existing works mostly handle with the problem in consideration of the
shape distortion, including perspective distortions, line curvature or other
style variations. Therefore, methods based on spatial transformers are
extensively studied. However, chromatic difficulties in complex scenes have not
been paid much attention on. In this work, we introduce a new learnable
geometric-unrelated module, the Structure-Preserving Inner Offset Network
(SPIN), which allows the color manipulation of source data within the network.
This differentiable module can be inserted before any recognition architecture
to ease the downstream tasks, giving neural networks the ability to actively
transform input intensity rather than the existing spatial rectification. It
can also serve as a complementary module to known spatial transformations and
work in both independent and collaborative ways with them. Extensive
experiments show that the use of SPIN results in a significant improvement on
multiple text recognition benchmarks compared to the state-of-the-arts.
