Papers › Text Spotting Transformers
Text Spotting Transformers
Xiang Zhang, Yongwen Su, Subarna Tripathi, Zhuowen Tu
In this paper, we present TExt Spotting TRansformers (TESTR), a generic end-to-end text spotting framework using Transformers for text detection and recognition in the wild. TESTR builds upon a single encoder and dual decoders for the joint text-box control point regression and character recognition. Other than most existing literature, our method is free from Region-of-Interest operations and heuristics-driven post-processing procedures; TESTR is particularly effective when dealing with curved text-boxes where special cares are needed for the adaptation of the traditional bounding-box representations. We show our canonical representation of control points suitable for text instances in both Bezier curve and polygon annotations. In addition, we design a bounding-box guided polygon detection (box-to-polygon) process. Experiments on curved and arbitrarily shaped datasets demonstrate state-of-the-art performances of the proposed TESTR algorithm.
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Code
Syntology Ran 7 of 12 code samples harvested from 1 repository linked to this paper; 5 have no recorded run. Of those that ran: 1 ran · our draft was wrong; 2 ran · fixture could not drive it; 4 ran with no contract checked.
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Code Syntology ran Syntology
12 samples harvested; 7 ran; 0 honoured the contract we drafted; 5 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text Spotting | ICDAR 2015 | TESTR | F-measure (%) - Generic Lexicon | 73.6 | #6 of 18 | Archive leaderboard | report |
| Text Spotting | ICDAR 2015 | TESTR | F-measure (%) - Strong Lexicon | 85.2 | #6 of 18 | Archive leaderboard | report |
| Text Spotting | ICDAR 2015 | TESTR | F-measure (%) - Weak Lexicon | 79.4 | #6 of 18 | Archive leaderboard | report |
| Text Spotting | Inverse-Text | TESTR | F-measure (%) - Full Lexicon | 41.6 | #8 of 9 | Archive leaderboard | report |
| Text Spotting | Inverse-Text | TESTR | F-measure (%) - No Lexicon | 34.2 | #8 of 9 | Archive leaderboard | report |
| Text Spotting | SCUT-CTW1500 | TESTR | F-Measure (%) - Full Lexicon | 81.5 | #9 of 11 | Archive leaderboard | report |
| Text Spotting | SCUT-CTW1500 | TESTR | F-measure (%) - No Lexicon | 56.0 | #9 of 11 | Archive leaderboard | report |
| Text Spotting | Total-Text | TESTR | F-measure (%) - Full Lexicon | 83.9 | #9 of 12 | Archive leaderboard | report |
| Text Spotting | Total-Text | TESTR | F-measure (%) - No Lexicon | 73.3 | #9 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.
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