Papers › Multi-Oriented Text Detection with Fully Convolutional Networks
Multi-Oriented Text Detection with Fully Convolutional Networks
Zheng Zhang, Chengquan Zhang, Wei Shen, Cong Yao, Wenyu Liu, Xiang Bai
In this paper, we propose a novel approach for text detec- tion in natural images. Both local and global cues are taken into account for localizing text lines in a coarse-to-fine pro- cedure. First, a Fully Convolutional Network (FCN) model is trained to predict the salient map of text regions in a holistic manner. Then, text line hypotheses are estimated by combining the salient map and character components. Fi- nally, another FCN classifier is used to predict the centroid of each character, in order to remove the false hypotheses. The framework is general for handling text in multiple ori- entations, languages and fonts. The proposed method con- sistently achieves the state-of-the-art performance on three text detection benchmarks: MSRA-TD500, ICDAR2015 and ICDAR2013.
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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 | SegLink | F-Measure | 75 | #40 of 43 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2015 | SegLink | Precision | 73.1 | #40 of 43 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2015 | SegLink | Recall | 76.8 | #40 of 43 | Archive leaderboard | report |
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