Robust Scene Text Recognition with Automatic Rectification
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 speciallydesigned 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.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)