  Recent approaches for end-to-end text spotting have achieved promising
results. However, most of the current spotters were plagued by the
inconsistency problem between text detection and recognition. In this work, we
introduce and prove the existence of the inconsistency problem and analyze it
from two aspects: (1) inconsistency of text recognition features between
training and testing, and (2) inconsistency of optimization targets between
text detection and recognition. To solve the aforementioned issues, we propose
a differentiable Auto-Rectification Module (ARM) together with a new training
strategy to enable propagating recognition loss back into detection branch, so
that our detection branch can be jointly optimized by detection and recognition
targets, which largely alleviates the inconsistency problem between text
detection and recognition. Based on these designs, we present a simple yet
robust end-to-end text spotting framework, termed Auto-Rectification Text
Spotter (ARTS), to detect and recognize arbitrarily-shaped text in natural
scenes. Extensive experiments demonstrate the superiority of our method. In
particular, our ARTS-S achieves 77.1% end-to-end text spotting F-measure on
Total-Text at a competitive speed of 10.5 FPS, which significantly outperforms
previous methods in both accuracy and inference speed.
