Table 2.
Comparison of Traditional and Deep Learning-Based Models on the Dongba1800 Dataset.
| Models | Precision(%) | Recall(%) | F-measure(%) | FPS |
|---|---|---|---|---|
| Traditional Models | ||||
| MSER | 2.84 | 79.91 | 5.47 | 6.74 |
| Sobel | 20.01 | 10.99 | 13.69 | 83.72 |
| Canny | 31.46 | 68.03 | 42.28 | 65.98 |
| MSE + NMS | 38.76 | 56.75 | 44.60 | 11.01 |
| Deep learning-based models | ||||
| FCENet | 76.13 | 65.60 | 70.48 | 0.72 |
| DBNet | 85.03 | 89.13 | 87.03 | 1.67 |
| PAN | 88.90 | 86.08 | 87.47 | 1.27 |
| PSENet | 89.70 | 85.97 | 87.79 | 1.25 |
| TextBPN++ | 86.52 | 89.89 | 87.93 | 6.74 |
| TextBPN | 89.40 | 88.50 | 88.66 | 6.33 |
| DBNet++ | 89.12 | 89.60 | 89.36 | 1.36 |
| MaskRCNN | 88.23 | 90.81 | 89.50 | 0.95 |