Papers › Scene Text Recognition with Permuted Autoregressive Sequence Models
Scene Text Recognition with Permuted Autoregressive Sequence Models
Darwin Bautista, Rowel Atienza
Context-aware STR methods typically use internal autoregressive (AR) language models (LM). Inherent limitations of AR models motivated two-stage methods which employ an external LM. The conditional independence of the external LM on the input image may cause it to erroneously rectify correct predictions, leading to significant inefficiencies. Our method, PARSeq, learns an ensemble of internal AR LMs with shared weights using Permutation Language Modeling. It unifies context-free non-AR and context-aware AR inference, and iterative refinement using bidirectional context. Using synthetic training data, PARSeq achieves state-of-the-art (SOTA) results in STR benchmarks (91.9% accuracy) and more challenging datasets. It establishes new SOTA results (96.0% accuracy) when trained on real data. PARSeq is optimal on accuracy vs parameter count, FLOPS, and latency because of its simple, unified structure and parallel token processing. Due to its extensive use of attention, it is robust on arbitrarily-oriented text which is common in real-world images. Code, pretrained weights, and data are available at: https://github.com/baudm/parseq.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Scene Text Recognition | COCO-Text | PARSeq | 1:1 Accuracy | 79.8±0.1 | #4 of 4 | Archive leaderboard | report |
| Scene Text Recognition | CUTE80 | PARSeq | Accuracy | 98.3±0.6 | #8 of 18 | Archive leaderboard | report |
| Scene Text Recognition | IC19-Art | PARSeq | Accuracy (%) | 84.5±0.1 | #5 of 5 | Archive leaderboard | report |
| Scene Text Recognition | ICDAR2013 | PARSeq | Accuracy | 98.4±0.2 | #6 of 38 | Archive leaderboard | report |
| Scene Text Recognition | ICDAR2015 | PARSeq | Accuracy | 89.6±0.3 | #8 of 27 | Archive leaderboard | report |
| Scene Text Recognition | IIIT5k | PARSeq | Accuracy | 99.1±0.1 | #7 of 17 | Archive leaderboard | report |
| Scene Text Recognition | SVT | PARSeq | Accuracy | 97.9±0.2 | #9 of 37 | Archive leaderboard | report |
| Scene Text Recognition | SVTP | PARSeq | Accuracy | 95.7±0.9 | #9 of 17 | 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.
Methods
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