Papers › Robust Scene Text Recognition with Automatic Rectification
Robust Scene Text Recognition with Automatic Rectification
Baoguang Shi, Xinggang Wang, Pengyuan Lyu, Cong Yao, Xiang Bai
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Scene Text Recognition | ICDAR 2003 | RARE | Accuracy | 90.1 | #10 of 12 | Archive leaderboard | report |
| Scene Text Recognition | ICDAR2013 | RARE | Accuracy | 88.6 | #36 of 38 | Archive leaderboard | report |
| Scene Text Recognition | SVT | RARE | Accuracy | 81.9 | #35 of 37 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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