Papers › Fused Text Segmentation Networks for Multi-oriented Scene Text Detection
Fused Text Segmentation Networks for Multi-oriented Scene Text Detection
Yuchen Dai, Zheng Huang, Yuting Gao, Youxuan Xu, Kai Chen, Jie Guo, Weidong Qiu
In this paper, we introduce a novel end-end framework for multi-oriented scene text detection from an instance-aware semantic segmentation perspective. We present Fused Text Segmentation Networks, which combine multi-level features during the feature extracting as text instance may rely on finer feature expression compared to general objects. It detects and segments the text instance jointly and simultaneously, leveraging merits from both semantic segmentation task and region proposal based object detection task. Not involving any extra pipelines, our approach surpasses the current state of the art on multi-oriented scene text detection benchmarks: ICDAR2015 Incidental Scene Text and MSRA-TD500 reaching Hmean 84.1% and 82.0% respectively. Morever, we report a baseline on total-text containing curved text which suggests effectiveness of the proposed approach.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Scene Text Detection | ICDAR 2015 | FTSN + MNMS | F-Measure | 84.1 | #28 of 43 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2015 | FTSN + MNMS | Precision | 88.6 | #28 of 43 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2015 | FTSN + MNMS | Recall | 80 | #28 of 43 | Archive leaderboard | report |
| Scene Text Detection | MSRA-TD500 | FTSN + MNMS | F-Measure | 82 | #12 of 18 | Archive leaderboard | report |
| Scene Text Detection | MSRA-TD500 | FTSN + MNMS | Precision | 87.6 | #12 of 18 | Archive leaderboard | report |
| Scene Text Detection | MSRA-TD500 | FTSN + MNMS | Recall | 77.1 | #12 of 18 | Archive leaderboard | report |
| Scene Text Detection | Total-Text | FTSN | F-Measure | 81.3% | #21 of 27 | Archive leaderboard | report |
| Scene Text Detection | Total-Text | FTSN | Precision | 84.7 | #21 of 27 | Archive leaderboard | report |
| Scene Text Detection | Total-Text | FTSN | Recall | 78 | #21 of 27 | 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.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections