Papers › Decoupled Attention Network for Text Recognition
Decoupled Attention Network for Text Recognition
Tianwei Wang, Yuanzhi Zhu, Lianwen Jin, Canjie Luo, Xiaoxue Chen, Yaqiang Wu, Qianying Wang, Mingxiang Cai
Text recognition has attracted considerable research interests because of its various applications. The cutting-edge text recognition methods are based on attention mechanisms. However, most of attention methods usually suffer from serious alignment problem due to its recurrency alignment operation, where the alignment relies on historical decoding results. To remedy this issue, we propose a decoupled attention network (DAN), which decouples the alignment operation from using historical decoding results. DAN is an effective, flexible and robust end-to-end text recognizer, which consists of three components: 1) a feature encoder that extracts visual features from the input image; 2) a convolutional alignment module that performs the alignment operation based on visual features from the encoder; and 3) a decoupled text decoder that makes final prediction by jointly using the feature map and attention maps. Experimental results show that DAN achieves state-of-the-art performance on multiple text recognition tasks, including offline handwritten text recognition and regular/irregular scene text recognition.
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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 |
|---|---|---|---|---|---|---|---|
| Handwritten Text Recognition | IAM | Decouple Attention Network | CER | 6.4 | #12 of 17 | Archive leaderboard | report |
| Handwritten Text Recognition | IAM | Decouple Attention Network | WER | 19.6 | #12 of 17 | Archive leaderboard | report |
| Scene Text Recognition | ICDAR 2003 | DAN | Accuracy | 95.0 | #4 of 12 | Archive leaderboard | report |
| Scene Text Recognition | ICDAR2013 | DAN | Accuracy | 93.9 | #25 of 38 | Archive leaderboard | report |
| Scene Text Recognition | ICDAR2015 | DAN | Accuracy | 74.5 | #23 of 27 | Archive leaderboard | report |
| Scene Text Recognition | SVT | DAN | Accuracy | 89.2 | #28 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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