Research map / Detection and segmentation / CNN features
PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add TDO to recover character reading order (ablation) | PGNet-A · ICDAR 2015 | Recognition accuracy: 61.9 -> 62.3; +0.4 | Helped |
| Add TDO to recover character reading order (ablation) | PGNet-E · ICDAR 2015 | Recognition accuracy: 56.9 -> 57.4; +0.5 | Helped |
| Add TDO to recover character reading order (ablation) | PGNet-A · Total-Text | Recognition accuracy: 60.4 -> 61.7; +1.3 | Helped |
| Add TDO to recover character reading order (ablation) | PGNet-E · Total-Text | Recognition accuracy: 56.8 -> 58.4; +1.6 | Helped |
| Add GRM for word-level semantic and visual context reasoning (ablation) | PGNet-A · ICDAR 2015 | Recognition accuracy: 62.3 -> 63.5; +1.2 | Helped |
| Add GRM for word-level semantic and visual context reasoning (ablation) | PGNet-E · ICDAR 2015 | Recognition accuracy: 57.4 -> 58.7; +1.3 | Helped |
| Add GRM for word-level semantic and visual context reasoning (ablation) | PGNet-A · Total-Text | Recognition accuracy: 61.7 -> 63.1; +1.4 | Helped |
| Add GRM for word-level semantic and visual context reasoning (ablation) | PGNet-E · Total-Text | Recognition accuracy: 58.4 -> 60.6; +2.2 | Helped |
Closest papers by meaning
- Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting (AAAI 2020 · similarity 0.87)
- LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Network (AAAI 2024 · similarity 0.85)
- Bridging the Gap Between End-to-End and Two-Step Text Spotting (CVPR 2024 · similarity 0.85)
- Arbitrary Reading Order Scene Text Spotter with Local Semantics Guidance (AAAI 2025 · similarity 0.84)
- SemiETS: Integrating Spatial and Content Consistencies for Semi-Supervised End-to-end Text Spotting (CVPR 2025 · similarity 0.84)
- GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching (NeurIPS 2024 · similarity 0.84)
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