Research map / Detection and segmentation / CNN features
A Text Detection System for Natural Scenes With Convolutional Feature Learning and Cascaded Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use rotation and aspect-ratio variations during fine-grained character detection (ablation) | Text-Conv system · ICDAR 2015 Focused Scene Text | word detection F-measure: 64.72% -> 86.77% | Helped |
| use the full cascaded system rather than StradVision | ICDAR 2015 Focused Scene Text word detection · ICDAR 2015 Focused Scene Text | word-bb F-Score: 85.20 -> 86.77 | Helped |
| use contextual neighboring-patch features in coarse detection | Text-Conv coarse character detector · ICDAR 2015 Focused Scene Text | detection discrimination | Helped |
| balance foreground and background samples when training Word-Graph | Word-Graph segmentation and character verification · ICDAR 2015 Focused Scene Text training samples | F-measure: 95% -> 99% | Helped |
Closest papers by meaning
- EAST: An Efficient and Accurate Scene Text Detector (CVPR 2017 · similarity 0.91)
- An End-to-End TextSpotter With Explicit Alignment and Attention (CVPR 2018 · similarity 0.90)
- Multi-Oriented Text Detection With Fully Convolutional Networks (CVPR 2016 · similarity 0.89)
- Character Region Awareness for Text Detection (CVPR 2019 · similarity 0.89)
- Kernel Adaptive Convolution for Scene Text Detection via Distance Map Prediction (CVPR 2024 · similarity 0.89)
- Multi-Scale FCN With Cascaded Instance Aware Segmentation for Arbitrary Oriented Word Spotting in the Wild (CVPR 2017 · similarity 0.89)
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