Ancient Text Restoration On I Phi
Métriques
CER (%)
Date (Years)
Region (Top 1 (%))
Region (Top 3 (%))
Top 1 (%)
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | CER (%) | Date (Years) | Region (Top 1 (%)) | Region (Top 3 (%)) | Top 1 (%) | Paper Title | Repository |
|---|---|---|---|---|---|---|---|
| Ithaca | 26.3 | 29.3 | 70.8 | 82.1 | 61.8 | Restoring and attributing ancient texts using deep neural networks | - |
| Pythia | 47.0 | - | - | - | 32.6 | Restoring and attributing ancient texts using deep neural networks | - |
| Onomastics | - | 144.4 | 21.2 | 26.5 | - | Restoring and attributing ancient texts using deep neural networks | - |
| Ancient historian and Ithaca | 18.3 | - | - | - | 71.7 | Restoring and attributing ancient texts using deep neural networks | - |
| Ancient historian | 59.6 | - | - | - | 25.3 | Restoring and attributing ancient texts using deep neural networks | - |
0 of 5 row(s) selected.