- Task 1 - Text Localization
- Task 2 - Script identification
- Task 3 - Joint text detection and script identification
method: PMTD2019-05-30
Authors: Jingchao Liu, Xuebo Liu, Ding Liang
Description: Pyramid Mask Text Detector, see https://arxiv.org/abs/1903.11800 for detail. Compared with the model mentioned in the paper, we add LSVT and ICDAR19 MLT datasets for training. Trained model and inference code will be released. If you have questions, please feel free to contact Jingchao Liu (liujingchao@sensetime.com) and Xuebo Liu (liuxuebo@sensetime.com)
method: PMTD2019-03-23
Authors: Jingchao Liu, Xuebo Liu
Description: Pyramid Mask Text Detector, see https://arxiv.org/abs/1903.11800 for detail. Trained model and inference code will be released. If you have questions, please feel free to contact Jingchao Liu (liujingchao@sensetime.com) and Xuebo Liu (liuxuebo@sensetime.com)
method: stela2019-07-15
Authors: Linjie Deng
Description: STELA is a simple and intuitive method for multi-oriented text detection based on RetinaNet. The key idea is utilizing the learned anchor which is obtained through a regression operation to replace the original into the final predictions.
| Date | Method | Hmean | Precision | Recall | Average Precision | |||
|---|---|---|---|---|---|---|---|---|
| 2019-05-30 | PMTD | 51.69% | 36.90% | 86.29% | 75.91% | |||
| 2019-03-23 | PMTD | 51.22% | 36.71% | 84.73% | 69.90% | |||
| 2019-07-15 | stela | 46.09% | 32.67% | 78.26% | 57.95% | |||
| 2018-05-18 | PSENet_NJU_ImagineLab (single-scale) | 45.98% | 32.86% | 76.53% | 25.15% | |||
| 2019-12-13 | BDN | 43.62% | 29.16% | 86.52% | 25.00% | |||
| 2017-06-29 | SARI_FDU_RRPN_v1 | 34.74% | 22.99% | 71.06% | 51.15% |