Handwritten text recognition on RIMES (test)
1.81CERDRetHTR_BASE
Evaluation results
| Method | Links | ||
|---|---|---|---|
| DRetHTR_BASEPre-training=English synthetic dataset, Fine-tuned=true2026.02 | 1.81 | — | |
| Coquenet et al.2026.02 | 1.91 | — | |
| Diaz et al.2026.02 | 1.99 | — | |
| DANRecognition type=Line-level, Input=Text lines2019.12 | 2.7 | 8.9 | |
| Retsinas et al.2026.02 | 2.7 | — | |
| Wang et al.2026.02 | 2.7 | — | |
| Bluche (2016)2019.12 | 2.9 | 12.6 | |
| CNN + 1D-LSTMLanguage Model=None, Lexicon=None, Data Augmentation=None, Citation=[5]2020.12 | 3.3 | — | |
| GFCNLanguage Model=None, Lexicon=None, Data Augmentation=None2020.12 | 4.35 | 18.01 | |
| CNN + 1D-LSTMLanguage Model=None, Lexicon=None, Data Augmentation=None, Citation=[6]2020.12 | 4.39 | 14.05 | |
| Sueiras et al. (2018)2019.12 | 4.8 | 15.9 | |
| 2D-LSTM-X2Language Model=None, Lexicon=None, Data Augmentation=None2020.12 | 4.8 | 16.42 | |
| 2D-LSTMLanguage Model=None, Lexicon=None, Data Augmentation=None2020.12 | 4.94 | 16.03 | |
| Bhunia et al. (2019)Recognition type=Word-level, Input=Cropped word images2019.12 | 6.4 | 10.5 | |
| Pham et al. (2014)2019.12 | 6.8 | 28.5 |