- 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: BDN2019-12-13
Authors: yl
Affiliation: SCUT
Description: We directly finetune our championship detection model of ReCTS on this dataset. The model and config files will be released on the source code website.
Updated version of the paper will discuss the very details of this model. The paper is in preparation.
| Date | Method | Hmean | Precision | Recall | Average Precision | |||
|---|---|---|---|---|---|---|---|---|
| 2019-05-30 | PMTD | 82.12% | 87.05% | 77.72% | 75.22% | |||
| 2019-03-23 | PMTD | 80.18% | 85.20% | 75.72% | 72.28% | |||
| 2019-12-13 | BDN | 79.47% | 82.75% | 76.44% | 63.08% | |||
| 2018-05-18 | PSENet_NJU_ImagineLab (single-scale) | 72.45% | 77.01% | 68.40% | 52.51% | |||
| 2019-07-15 | stela | 71.50% | 78.68% | 65.52% | 60.26% | |||
| 2017-06-29 | SARI_FDU_RRPN_v1 | 62.37% | 71.17% | 55.50% | 50.33% |