Papers › Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting
Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting
Liang Qiao, Sanli Tang, Zhanzhan Cheng, Yunlu Xu, Yi Niu, ShiLiang Pu, Fei Wu
Many approaches have recently been proposed to detect irregular scene text and achieved promising results. However, their localization results may not well satisfy the following text recognition part mainly because of two reasons: 1) recognizing arbitrary shaped text is still a challenging task, and 2) prevalent non-trainable pipeline strategies between text detection and text recognition will lead to suboptimal performances. To handle this incompatibility problem, in this paper we propose an end-to-end trainable text spotting approach named Text Perceptron. Concretely, Text Perceptron first employs an efficient segmentation-based text detector that learns the latent text reading order and boundary information. Then a novel Shape Transform Module (abbr. STM) is designed to transform the detected feature regions into regular morphologies without extra parameters. It unites text detection and the following recognition part into a whole framework, and helps the whole network achieve global optimization. Experiments show that our method achieves competitive performance on two standard text benchmarks, i.e., ICDAR 2013 and ICDAR 2015, and also obviously outperforms existing methods on irregular text benchmarks SCUT-CTW1500 and Total-Text.
In Syntology View this paper on Syntology: its repositories, every harvested function with whether it ran, its licence and the call to fetch it.
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
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text Spotting | ICDAR 2015 | TextPerceptron | F-measure (%) - Generic Lexicon | 65.1 | #17 of 18 | Archive leaderboard | report |
| Text Spotting | ICDAR 2015 | TextPerceptron | F-measure (%) - Strong Lexicon | 80.5 | #17 of 18 | Archive leaderboard | report |
| Text Spotting | ICDAR 2015 | TextPerceptron | F-measure (%) - Weak Lexicon | 76.6 | #17 of 18 | Archive leaderboard | report |
| Text Spotting | SCUT-CTW1500 | TextPerceptron | F-measure (%) - No Lexicon | 57.0 | #8 of 11 | 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