Papers › Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with...
Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes
Pengyuan Lyu, Minghui Liao, Cong Yao, Wenhao Wu, Xiang Bai
Recently, models based on deep neural networks have dominated the fields of scene text detection and recognition. In this paper, we investigate the problem of scene text spotting, which aims at simultaneous text detection and recognition in natural images. An end-to-end trainable neural network model for scene text spotting is proposed. The proposed model, named as Mask TextSpotter, is inspired by the newly published work Mask R-CNN. Different from previous methods that also accomplish text spotting with end-to-end trainable deep neural networks, Mask TextSpotter takes advantage of simple and smooth end-to-end learning procedure, in which precise text detection and recognition are acquired via semantic segmentation. Moreover, it is superior to previous methods in handling text instances of irregular shapes, for example, curved text. Experiments on ICDAR2013, ICDAR2015 and Total-Text demonstrate that the proposed method achieves state-of-the-art results in both scene text detection and end-to-end text recognition tasks.
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 Detection | ICDAR 2013 | Mask TextSpotter | F-Measure | 91.7% | #3 of 16 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2013 | Mask TextSpotter | Precision | 95 | #3 of 16 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2013 | Mask TextSpotter | Recall | 88.6 | #3 of 16 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2015 | Mask TextSpotter | F-Measure | 86 | #21 of 43 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2015 | Mask TextSpotter | Precision | 91.6 | #21 of 43 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2015 | Mask TextSpotter | Recall | 81 | #21 of 43 | Archive leaderboard | report |
| Scene Text Detection | Total-Text | Mask TextSpotter | F-Measure | 61.3% | #25 of 27 | Archive leaderboard | report |
| Scene Text Detection | Total-Text | Mask TextSpotter | Precision | 69 | #25 of 27 | Archive leaderboard | report |
| Scene Text Detection | Total-Text | Mask TextSpotter | Recall | 55 | #25 of 27 | Archive leaderboard | report |
| Text Spotting | Inverse-Text | MaskTextSpotter v2 | F-measure (%) - Full Lexicon | 43.5 | #5 of 9 | Archive leaderboard | report |
| Text Spotting | Inverse-Text | MaskTextSpotter v2 | F-measure (%) - No Lexicon | 39.0 | #5 of 9 | 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.
Methods
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