Papers › Geometry Normalization Networks for Accurate Scene Text Detection
Geometry Normalization Networks for Accurate Scene Text Detection
Youjiang Xu, Jiaqi Duan, Zhanghui Kuang, Xiaoyu Yue, Hongbin Sun, Yue Guan, Wayne Zhang
Large geometry (e.g., orientation) variances are the key challenges in the scene text detection. In this work, we first conduct experiments to investigate the capacity of networks for learning geometry variances on detecting scene texts, and find that networks can handle only limited text geometry variances. Then, we put forward a novel Geometry Normalization Module (GNM) with multiple branches, each of which is composed of one Scale Normalization Unit and one Orientation Normalization Unit, to normalize each text instance to one desired canonical geometry range through at least one branch. The GNM is general and readily plugged into existing convolutional neural network based text detectors to construct end-to-end Geometry Normalization Networks (GNNets). Moreover, we propose a geometry-aware training scheme to effectively train the GNNets by sampling and augmenting text instances from a uniform geometry variance distribution. Finally, experiments on popular benchmarks of ICDAR 2015 and ICDAR 2017 MLT validate that our method outperforms all the state-of-the-art approaches remarkably by obtaining one-forward test F-scores of 88.52 and 74.54 respectively.
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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 | GNNets | F-Measure | 88.52 | #11 of 43 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2015 | GNNets | Precision | 90.41 | #11 of 43 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2015 | GNNets | Recall | 86.71 | #11 of 43 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2017 MLT | GNNets | F-Measure | 74.54% | #10 of 14 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2017 MLT | GNNets | Precision | 79.63 | #10 of 14 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2017 MLT | GNNets | Recall | 70.06 | #10 of 14 | 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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