Papers › SPTS: Single-Point Text Spotting
SPTS: Single-Point Text Spotting
Dezhi Peng, Xinyu Wang, Yuliang Liu, Jiaxin Zhang, Mingxin Huang, Songxuan Lai, Shenggao Zhu, Jing Li, Dahua Lin, Chunhua Shen, Xiang Bai, Lianwen Jin
Existing scene text spotting (i.e., end-to-end text detection and recognition) methods rely on costly bounding box annotations (e.g., text-line, word-level, or character-level bounding boxes). For the first time, we demonstrate that training scene text spotting models can be achieved with an extremely low-cost annotation of a single-point for each instance. We propose an end-to-end scene text spotting method that tackles scene text spotting as a sequence prediction task. Given an image as input, we formulate the desired detection and recognition results as a sequence of discrete tokens and use an auto-regressive Transformer to predict the sequence. The proposed method is simple yet effective, which can achieve state-of-the-art results on widely used benchmarks. Most significantly, we show that the performance is not very sensitive to the positions of the point annotation, meaning that it can be much easier to be annotated or even be automatically generated than the bounding box that requires precise positions. We believe that such a pioneer attempt indicates a significant opportunity for scene text spotting applications of a much larger scale than previously possible. The code is available at https://github.com/shannanyinxiang/SPTS.
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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 |
|---|---|---|---|---|---|---|---|
| Text Spotting | ICDAR 2015 | SPTS | F-measure (%) - Generic Lexicon | 65.8 | #18 of 18 | Archive leaderboard | report |
| Text Spotting | ICDAR 2015 | SPTS | F-measure (%) - Strong Lexicon | 77.5 | #18 of 18 | Archive leaderboard | report |
| Text Spotting | ICDAR 2015 | SPTS | F-measure (%) - Weak Lexicon | 70.2 | #18 of 18 | Archive leaderboard | report |
| Text Spotting | Inverse-Text | SPTS | F-measure (%) - Full Lexicon | 46.2 | #6 of 9 | Archive leaderboard | report |
| Text Spotting | Inverse-Text | SPTS | F-measure (%) - No Lexicon | 38.3 | #6 of 9 | Archive leaderboard | report |
| Text Spotting | SCUT-CTW1500 | SPTS | F-Measure (%) - Full Lexicon | 83.8 | #3 of 11 | Archive leaderboard | report |
| Text Spotting | SCUT-CTW1500 | SPTS | F-measure (%) - No Lexicon | 63.6 | #3 of 11 | 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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