Papers › Detecting Oriented Text in Natural Images by Linking Segments
Detecting Oriented Text in Natural Images by Linking Segments
Baoguang Shi, Xiang Bai, Serge Belongie
Most state-of-the-art text detection methods are specific to horizontal Latin text and are not fast enough for real-time applications. We introduce Segment Linking (SegLink), an oriented text detection method. The main idea is to decompose text into two locally detectable elements, namely segments and links. A segment is an oriented box covering a part of a word or text line; A link connects two adjacent segments, indicating that they belong to the same word or text line. Both elements are detected densely at multiple scales by an end-to-end trained, fully-convolutional neural network. Final detections are produced by combining segments connected by links. Compared with previous methods, SegLink improves along the dimensions of accuracy, speed, and ease of training. It achieves an f-measure of 75.0% on the standard ICDAR 2015 Incidental (Challenge 4) benchmark, outperforming the previous best by a large margin. It runs at over 20 FPS on 512x512 images. Moreover, without modification, SegLink is able to detect long lines of non-Latin text, such as Chinese.
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4201fce83949ddcd · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Scene Text Detection | ICDAR 2013 | SegLink | F-Measure | 85.3% | #11 of 16 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2013 | SegLink | Precision | 87.7 | #11 of 16 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2013 | SegLink | Recall | 83 | #11 of 16 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2015 | WordSup (VGG16-synth-icdar) | F-Measure | 78.2 | #38 of 43 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2015 | WordSup (VGG16-synth-icdar) | Precision | 79.3 | #38 of 43 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2015 | WordSup (VGG16-synth-icdar) | Recall | 77.0 | #38 of 43 | Archive leaderboard | report |
| Scene Text Detection | MSRA-TD500 | SegLink | F-Measure | 77 | #17 of 18 | Archive leaderboard | report |
| Scene Text Detection | MSRA-TD500 | SegLink | Precision | 86 | #17 of 18 | Archive leaderboard | report |
| Scene Text Detection | MSRA-TD500 | SegLink | Recall | 70 | #17 of 18 | 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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