Research map / Detection and segmentation / CNN features
Multi-Oriented Text Detection With Fully Convolutional Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed FCN and MSER coarse-to-fine text detector | Scene text detection · MSRA-TD500 test | F-measure: 0.71 -> 0.74 | Helped |
| Use the proposed FCN and MSER coarse-to-fine text detector | Scene text detection · ICDAR2015 test | F-measure: 0.54 vs 0.50 | Helped |
| Use the proposed FCN and MSER coarse-to-fine text detector | Scene text detection · ICDAR2013 test | F-measure: 0.83 vs 0.80 | Helped |
| Vary MSER component thresholds T1 and T2 | Proposed detector · MSRA-TD500 | candidate recall | No clear effect |
Closest papers by meaning
- An End-to-End TextSpotter With Explicit Alignment and Attention (CVPR 2018 · similarity 0.90)
- Multi-Scale FCN With Cascaded Instance Aware Segmentation for Arbitrary Oriented Word Spotting in the Wild (CVPR 2017 · similarity 0.89)
- A Text Detection System for Natural Scenes With Convolutional Feature Learning and Cascaded Classification (CVPR 2016 · similarity 0.89)
- Kernel Adaptive Convolution for Scene Text Detection via Distance Map Prediction (CVPR 2024 · similarity 0.86)
- General Detection-based Text Line Recognition (NeurIPS 2024 · similarity 0.84)
- LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Network (AAAI 2024 · similarity 0.84)
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