Papers › ASTER: An Attentional Scene Text Recognizer with Flexible Rectification
ASTER: An Attentional Scene Text Recognizer with Flexible Rectification
Baoguang Shi, Mingkun Yang, Xinggang Wang, Pengyuan Lyu, Cong Yao, and Xiang Bai
SCENE text recognition has attracted great interest from the academia and the industry in recent years owing to its importance in a wide range of applications. Despite the maturity of Optical Character Recognition (OCR) systems dedicated to document text, scene text recognition remains a challenging problem. The large variations in background, appearance, and layout pose significant challenges, which the traditional OCR methods cannot handle effectively. Recent advances in scene text recognition are driven by the success of deep learning-based recognition models. Among them are methods that recognize text by characters using convolutional neural networks (CNN), methods that classify words with CNNs [24], [26], and methods that recognize character sequences using a combination of a CNN and a recurrent neural network (RNN) [54]. In spite of their success, these methods do not explicitly address the problem of irregular text, which is text that is not horizontal and frontal, has curved layout, etc. Instances of irregular text frequently appear in natural scenes. As exemplified in Figure 1, typical cases include oriented text, perspective text [49], and curved text. Designed without the invariance to such irregularities, previous methods often struggle in recognizing such text instances.
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 | ICDAR2013 | ASTER | Accuracy | 91.8 | #32 of 38 | Archive leaderboard | report |
| Scene Text Recognition | ICDAR2015 | ASTER | Accuracy | 76.1 | #22 of 27 | Archive leaderboard | report |
| Scene Text Recognition | SVT | ASTER | Accuracy | 89.5 | #27 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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