- 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: PSENet_NJU_ImagineLab (single-scale)2018-05-18
Authors: Wenhai Wang, Xiang Li, Wenbo Hou, Tong Lu, Jian Yang
Description: A text detector based on semantic segmentation. Using only ICDAR_2017 MLT training set and ICDAR 2015 training set. Paper is in the preparation. And we will release our code latter.
Date | Method | Hmean | Precision | Recall | Average Precision | |||
---|---|---|---|---|---|---|---|---|
2019-05-30 | PMTD | 38.51% | 24.22% | 93.95% | 82.23% | |||
2019-03-23 | PMTD | 37.55% | 23.71% | 90.18% | 49.86% | |||
2018-05-18 | PSENet_NJU_ImagineLab (single-scale) | 33.21% | 20.94% | 80.16% | 17.24% | |||
2019-07-15 | stela | 32.40% | 20.21% | 81.69% | 60.02% | |||
2019-12-13 | BDN | 30.57% | 18.26% | 93.71% | 18.50% | |||
2017-06-29 | SARI_FDU_RRPN_v1 | 26.38% | 15.53% | 87.39% | 61.20% |