  Reading text from natural images is challenging due to the great variety in
text font, color, size, complex background and etc.. The perspective distortion
and non-linear spatial arrangement of characters make it further difficult.
While rectification based method is intuitively grounded and has pushed the
envelope by far, its potential is far from being well exploited. In this paper,
we present a bag of tricks that prove to significantly improve the performance
of rectification based method. On curved text dataset, our method achieves an
accuracy of 89.6% on CUTE-80 and 76.3% on Total-Text, an improvement over
previous state-of-the-art by 6.3% and 14.7% respectively. Furthermore, our
combination of tricks helps us win the ICDAR 2019 Arbitrary-Shaped Text
Challenge (Latin script), achieving an accuracy of 74.3% on the held-out test
set. We release our code as well as data samples for further exploration at
https://github.com/Jyouhou/ICDAR2019-ArT-Recognition-Alchemy
