Papers › PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network
PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network
Pengfei Wang, Chengquan Zhang, Fei Qi, Shanshan Liu, Xiaoqiang Zhang, Pengyuan Lyu, Junyu Han, Jingtuo Liu, Errui Ding, Guangming Shi
The reading of arbitrarily-shaped text has received increasing research attention. However, existing text spotters are mostly built on two-stage frameworks or character-based methods, which suffer from either Non-Maximum Suppression (NMS), Region-of-Interest (RoI) operations, or character-level annotations. In this paper, to address the above problems, we propose a novel fully convolutional Point Gathering Network (PGNet) for reading arbitrarily-shaped text in real-time. The PGNet is a single-shot text spotter, where the pixel-level character classification map is learned with proposed PG-CTC loss avoiding the usage of character-level annotations. With a PG-CTC decoder, we gather high-level character classification vectors from two-dimensional space and decode them into text symbols without NMS and RoI operations involved, which guarantees high efficiency. Additionally, reasoning the relations between each character and its neighbors, a graph refinement module (GRM) is proposed to optimize the coarse recognition and improve the end-to-end performance. Experiments prove that the proposed method achieves competitive accuracy, meanwhile significantly improving the running speed. In particular, in Total-Text, it runs at 46.7 FPS, surpassing the previous spotters with a large margin.
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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 Detection | ICDAR 2015 | MCLAB_FCN | F-Measure | 53.6 | #41 of 43 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2015 | MCLAB_FCN | Precision | 70.8 | #41 of 43 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2015 | MCLAB_FCN | Recall | 43.0 | #41 of 43 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2015 | PGNet-A | Accuracy | 62.3 | #42 of 43 | Archive leaderboard | report |
| Text Spotting | ICDAR 2015 | PGNet | F-measure (%) - Generic Lexicon | 63.5 | #11 of 18 | Archive leaderboard | report |
| Text Spotting | ICDAR 2015 | PGNet | F-measure (%) - Strong Lexicon | 83.3 | #11 of 18 | Archive leaderboard | report |
| Text Spotting | ICDAR 2015 | PGNet | F-measure (%) - Weak Lexicon | 78.3 | #11 of 18 | 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
Introduced by this paper: PGNet
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