  Scene text recognition has received increased attention in the research
community. Text in the wild often possesses irregular arrangements, typically
including perspective text, curved text, oriented text. Most existing methods
are hard to work well for irregular text, especially for severely distorted
text. In this paper, we propose a novel Recurrent Calibration Network (RCN) for
irregular scene text recognition. The RCN progressively calibrates the
irregular text to boost the recognition performance. By decomposing the
calibration process into multiple steps, the irregular text can be calibrated
to normal one step by step. Besides, in order to avoid the accumulation of lost
information caused by inaccurate transformation, we further design a
fiducial-point refinement structure to keep the integrity of text during the
recurrent process. Instead of the calibrated images, the coordinates of
fiducial points are tracked and refined, which implicitly models the
transformation information. Based on the refined fiducial points, we estimate
the transformation parameters and sample from the original image at each step.
In this way, the original character information is preserved until the final
transformation. Such designs lead to optimal calibration results to boost the
performance of succeeding recognition. Extensive experiments on challenging
datasets demonstrate the superiority of our method, especially on irregular
benchmarks.
