  Recognizing text in natural images is a challenging task with many unsolved
problems. Different from those in documents, words in natural images often
possess irregular shapes, which are caused by perspective distortion, curved
character placement, etc. We propose RARE (Robust text recognizer with
Automatic REctification), a recognition model that is robust to irregular text.
RARE is a specially-designed deep neural network, which consists of a Spatial
Transformer Network (STN) and a Sequence Recognition Network (SRN). In testing,
an image is firstly rectified via a predicted Thin-Plate-Spline (TPS)
transformation, into a more "readable" image for the following SRN, which
recognizes text through a sequence recognition approach. We show that the model
is able to recognize several types of irregular text, including perspective
text and curved text. RARE is end-to-end trainable, requiring only images and
associated text labels, making it convenient to train and deploy the model in
practical systems. State-of-the-art or highly-competitive performance achieved
on several benchmarks well demonstrates the effectiveness of the proposed
model.
