Papers › Multi-Granularity Prediction for Scene Text Recognition
Multi-Granularity Prediction for Scene Text Recognition
Peng Wang, Cheng Da, Cong Yao
Scene text recognition (STR) has been an active research topic in computer vision for years. To tackle this challenging problem, numerous innovative methods have been successively proposed and incorporating linguistic knowledge into STR models has recently become a prominent trend. In this work, we first draw inspiration from the recent progress in Vision Transformer (ViT) to construct a conceptually simple yet powerful vision STR model, which is built upon ViT and outperforms previous state-of-the-art models for scene text recognition, including both pure vision models and language-augmented methods. To integrate linguistic knowledge, we further propose a Multi-Granularity Prediction strategy to inject information from the language modality into the model in an implicit way, i.e. , subword representations (BPE and WordPiece) widely-used in NLP are introduced into the output space, in addition to the conventional character level representation, while no independent language model (LM) is adopted. The resultant algorithm (termed MGP-STR) is able to push the performance envelop of STR to an even higher level. Specifically, it achieves an average recognition accuracy of 93.35% on standard benchmarks. Code is available at https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/MGP-STR.
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Code
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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 | MGP-STR | 1:1 Accuracy | 81.7 | #2 of 4 | Archive leaderboard | report |
| Scene Text Recognition | CUTE80 | MGP-STR | Accuracy | 99.31 | #4 of 18 | Archive leaderboard | report |
| Scene Text Recognition | IC19-Art | MGP-STR | Accuracy (%) | 85.5 | #4 of 5 | Archive leaderboard | report |
| Scene Text Recognition | ICDAR2013 | MGP-STR | Accuracy | 98.5 | #4 of 38 | Archive leaderboard | report |
| Scene Text Recognition | ICDAR2015 | MGP-STR | Accuracy | 90.9 | #5 of 27 | Archive leaderboard | report |
| Scene Text Recognition | IIIT5k | MGP-STR | Accuracy | 98.8 | #8 of 17 | Archive leaderboard | report |
| Scene Text Recognition | SVT | MGP-STR | Accuracy | 98.6 | #4 of 37 | Archive leaderboard | report |
| Scene Text Recognition | SVTP | MGP-STR | Accuracy | 98.3 | #2 of 17 | Archive leaderboard | report |
| Scene Text Recognition | Uber-Text | MGP-STR | Accuracy (%) | 91.0 | #2 of 3 | 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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