Papers › MANGO: A Mask Attention Guided One-Stage Scene Text Spotter
MANGO: A Mask Attention Guided One-Stage Scene Text Spotter
Liang Qiao, Ying Chen, Zhanzhan Cheng, Yunlu Xu, Yi Niu, ShiLiang Pu, Fei Wu
Recently end-to-end scene text spotting has become a popular research topic due to its advantages of global optimization and high maintainability in real applications. Most methods attempt to develop various region of interest (RoI) operations to concatenate the detection part and the sequence recognition part into a two-stage text spotting framework. However, in such framework, the recognition part is highly sensitive to the detected results (e.g.), the compactness of text contours). To address this problem, in this paper, we propose a novel Mask AttentioN Guided One-stage text spotting framework named MANGO, in which character sequences can be directly recognized without RoI operation. Concretely, a position-aware mask attention module is developed to generate attention weights on each text instance and its characters. It allows different text instances in an image to be allocated on different feature map channels which are further grouped as a batch of instance features. Finally, a lightweight sequence decoder is applied to generate the character sequences. It is worth noting that MANGO inherently adapts to arbitrary-shaped text spotting and can be trained end-to-end with only coarse position information (e.g.), rectangular bounding box) and text annotations. Experimental results show that the proposed method achieves competitive and even new state-of-the-art performance on both regular and irregular text spotting benchmarks, i.e., ICDAR 2013, ICDAR 2015, Total-Text, and SCUT-CTW1500.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text Spotting | ICDAR 2015 | MANGO | F-measure (%) - Generic Lexicon | 67.3 | #16 of 18 | Archive leaderboard | report |
| Text Spotting | ICDAR 2015 | MANGO | F-measure (%) - Strong Lexicon | 81.8 | #16 of 18 | Archive leaderboard | report |
| Text Spotting | ICDAR 2015 | MANGO | F-measure (%) - Weak Lexicon | 78.9 | #16 of 18 | Archive leaderboard | report |
| Text Spotting | SCUT-CTW1500 | MANGO | F-Measure (%) - Full Lexicon | 78.7 | #6 of 11 | Archive leaderboard | report |
| Text Spotting | SCUT-CTW1500 | MANGO | F-measure (%) - No Lexicon | 58.9 | #6 of 11 | Archive leaderboard | report |
| Text Spotting | Total-Text | MANGO | F-measure (%) - Full Lexicon | 83.6 | #10 of 12 | Archive leaderboard | report |
| Text Spotting | Total-Text | MANGO | F-measure (%) - No Lexicon | 72.9 | #10 of 12 | 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