arXiv:1603.03915cs.CV
Robust Scene Text Recognition with Automatic Rectification
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Abstract
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.
Current state-of-the-art approaches in this field are significantly better than existing commercial solutions at recognizing text in the wild (i.e. scene text). As an example, see the ICDAR 2015 results [1], where the Google Vision API is at 59.60% (Hmean) while the best ones…
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If you're trying to handle text "in the wild" and not scanned documents, the keyword is "scene text". Most papers are focused on either detection/localization, i.e. finding the location of text, or recognition, i.e. recognizing the actual content given a cropped text image. Here…
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