  Automated recognition of texts in scenes has been a research challenge for
years, largely due to the arbitrary variation of text appearances in
perspective distortion, text line curvature, text styles and different types of
imaging artifacts. The recent deep networks are capable of learning robust
representations with respect to imaging artifacts and text style changes, but
still face various problems while dealing with scene texts with perspective and
curvature distortions. This paper presents an end-to-end trainable scene text
recognition system (ESIR) that iteratively removes perspective distortion and
text line curvature as driven by better scene text recognition performance. An
innovative rectification network is developed which employs a novel
line-fitting transformation to estimate the pose of text lines in scenes. In
addition, an iterative rectification pipeline is developed where scene text
distortions are corrected iteratively towards a fronto-parallel view. The ESIR
is also robust to parameter initialization and the training needs only scene
text images and word-level annotations as required by most scene text
recognition systems. Extensive experiments over a number of public datasets
show that the proposed ESIR is capable of rectifying scene text distortions
accurately, achieving superior recognition performance for both normal scene
text images and those suffering from perspective and curvature distortions.
