icdar-2017-mlt is a state-of-the-art machine learning benchmark indexed on Codesota. This page tracks published model results, top scores per metric, and the SOTA timeline for icdar-2017-mlt.
Precision is the reported evaluation metric for icdar-2017-mlt. Codesota tracks published model scores on this metric so readers can compare state-of-the-art results across sources and model families.
Higher is better
F Measure is the reported evaluation metric for icdar-2017-mlt. Codesota tracks published model scores on this metric so readers can compare state-of-the-art results across sources and model families.
Higher is better
| Rank | Model | Trust | Score | Year | Links | Fix |
|---|---|---|---|---|---|---|
| 01 | PMTD* | verified | 80.13 | 2019 | Paper ↗Code ↗ | Looks wrong? |
| 02 | SBD | verified | 79.47 | 2019 | Paper ↗Code ↗ | Looks wrong? |
| 03 | TextBPN++ (ResNet-50+DCN) | verified | 77.48 | 2022 | Paper ↗ | Looks wrong? |
| 04 | CharNet H-88 | verified | 75.77 | 2019 | Paper ↗Code ↗ | Looks wrong? |
| 05 | DBNet (ResNet-50+DCN) | verified | 74.7 | 2022 | Paper ↗ | Looks wrong? |
| 06 | GNNets | verified | 74.54 | 2019 | Paper ↗Code ↗ | Looks wrong? |
| 07 | PAN | verified | 74.3 | 2018 | Paper ↗ | Looks wrong? |
| 08 | SPCNET | verified | 74.1 | 2018 | Paper ↗Code ↗ | Looks wrong? |
| 09 | CharNet R-50 | verified | 73.42 | 2019 | Paper ↗Code ↗ | Looks wrong? |
| 10 | PSENet-1s | verified | 72.45 | 2018 | Paper ↗Code ↗ | Looks wrong? |
| 11 | Corner Localization (multi-scale) | verified | 72.4 | 2018 | Paper ↗Code ↗ | Looks wrong? |
| 12 | TextBPN++ (ResNet-50) | verified | 72.33 | 2022 | Paper ↗ | Looks wrong? |
| 13 | PSENet (ResNet-152) | verified | 72.13 | 2019 | Paper ↗Code ↗ | Looks wrong? |
| 14 | DBNet (ResNet-18+DCN) | verified | 71.7 | 2022 | Paper ↗ | Looks wrong? |
| 15 | FOTS MS | verified | 70.75 | 2018 | Paper ↗Code ↗ | Looks wrong? |
| 16 | FOTS | verified | 67.25 | 2018 | Paper ↗Code ↗ | Looks wrong? |
| 17 | Corner Localization (single-scale) | verified | 66.8 | 2018 | Paper ↗Code ↗ | Looks wrong? |
Recall is the reported evaluation metric for icdar-2017-mlt. Codesota tracks published model scores on this metric so readers can compare state-of-the-art results across sources and model families.
Higher is better
H Mean is the reported evaluation metric for icdar-2017-mlt. Codesota tracks published model scores on this metric so readers can compare state-of-the-art results across sources and model families.
Higher is better
| Rank | Model | Trust | Score | Year | Links | Fix |
|---|---|---|---|---|---|---|
| 01 | CRAFT | verified | 73.9 | 2019 | Paper ↗Code ↗ | Looks wrong? |