- Task 1 - Text Localization
- Task 2 - Script identification
- Task 3 - Joint text detection and script identification
method: Sogou_OCR2019-11-08
Authors: Xudong Rao, Lulu Xu, Long Ma, Xuefeng Su
Description: An arbitrary-shaped text detection method based on Mask R-CNN, we use resnext-152 as our backbone, multi-scale training and testing are adopted to get the final results.
method: AntAI-Cognition2019-09-17
Authors: Qingpei Guo, Yudong Liu, Yonggang Li, Wei Zhang, Yongtao Wang, Jingdong Chen, Wei Chu
Affiliation: Ant Financial AI department & PKU
Description: We are from AntAI & PKU, it's a HTC-based ensemble method.
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)
Date | Method | Hmean | Precision | Recall | Average Precision | |||
---|---|---|---|---|---|---|---|---|
2019-11-08 | Sogou_OCR | 69.37% | 60.97% | 80.46% | 65.51% | |||
2019-09-17 | AntAI-Cognition | 66.55% | 57.19% | 79.57% | 53.22% | |||
2019-05-30 | PMTD | 64.77% | 55.26% | 78.23% | 63.79% | |||
2019-08-08 | JDAI | 64.38% | 55.33% | 76.96% | 61.58% | |||
2019-05-08 | Baidu-VIS | 64.31% | 55.42% | 76.59% | 41.52% | |||
2019-03-23 | PMTD | 63.05% | 53.85% | 76.05% | 61.26% | |||
2019-11-05 | baseline_maskrcnn | 62.72% | 53.14% | 76.52% | 59.67% | |||
2019-08-20 | juxinli | 62.26% | 53.63% | 74.20% | 59.85% | |||
2019-06-11 | 4Paradigm-Data-Intelligence | 62.20% | 50.86% | 80.06% | 39.66% | |||
2019-06-02 | NJU-ImagineLab | 61.30% | 51.50% | 75.70% | 58.97% | |||
2019-05-23 | 4Paradigm-Data-Intelligence | 61.04% | 50.33% | 77.53% | 37.92% | |||
2019-03-19 | ccnet single scale | 57.62% | 49.65% | 68.63% | 47.01% | |||
2018-11-20 | Pixel-Anchor | 56.66% | 50.77% | 64.09% | 34.21% | |||
2019-03-29 | GNNets (single scale) | 56.54% | 49.30% | 66.29% | 44.67% | |||
2019-12-13 | BDN | 56.27% | 44.91% | 75.30% | 32.80% | |||
2018-10-29 | Amap-CVLab | 56.25% | 47.01% | 70.00% | 49.59% | |||
2018-11-15 | USTC-NELSLIP | 55.58% | 43.95% | 75.59% | 60.29% | |||
2018-01-22 | FOTS_v2 | 55.33% | 47.58% | 66.08% | 46.29% | |||
2018-11-28 | CRAFT | 55.23% | 48.07% | 64.89% | 30.47% | |||
2018-05-18 | PSENet_NJU_ImagineLab (single-scale) | 54.57% | 45.44% | 68.28% | 30.21% | |||
2018-12-22 | PKU_VDIG | 53.84% | 41.91% | 75.29% | 54.46% | |||
2019-07-15 | stela | 53.32% | 45.31% | 64.76% | 45.50% | |||
2018-03-12 | ATL Cangjie OCR | 53.18% | 42.20% | 71.88% | 56.17% | |||
2018-12-04 | SPCNet_TongJi & UESTC (multi scale) | 52.06% | 43.26% | 65.38% | 27.39% | |||
2017-06-28 | SCUT_DLVClab1 | 51.27% | 45.80% | 58.24% | 38.59% | |||
2017-11-09 | EAST++ | 51.07% | 43.27% | 62.30% | 29.75% | |||
2019-01-08 | ALGCD_CP | 50.60% | 41.17% | 65.65% | 27.30% | |||
2018-08-23 | Sogou_MM | 50.35% | 40.40% | 66.81% | 44.72% | |||
2019-09-18 | mask RCNN Augment+ | 50.23% | 45.24% | 56.46% | 36.66% | |||
2018-12-02 | Shape-Aware Based Scene Text Detector (single scale) | 49.96% | 40.58% | 65.00% | 25.68% | |||
2019-05-30 | Thesis-SE | 45.58% | 37.34% | 58.49% | 23.54% | |||
2018-12-13 | AutoCV | 45.01% | 33.36% | 69.15% | 41.95% | |||
2018-12-03 | SPCNet_TongJi & UESTC (single scale) | 44.38% | 32.88% | 68.23% | 21.78% | |||
2018-12-05 | EPTN-SJTU | 43.88% | 36.03% | 56.10% | 21.90% | |||
2017-06-29 | SARI_FDU_RRPN_v1 | 39.40% | 32.19% | 50.76% | 24.45% | |||
2017-06-28 | SARI_FDU_RRPN_v0 | 38.55% | 28.94% | 57.72% | 30.92% | |||
2017-06-30 | TH-DL | 32.73% | 30.16% | 35.78% | 20.68% | |||
2019-01-03 | YY AI OCR Group | 31.43% | 25.24% | 41.65% | 13.07% | |||
2017-06-30 | Sensetime OCR | 27.39% | 17.11% | 68.68% | 44.78% | |||
2017-06-30 | linkage-ER-Flow | 14.40% | 10.71% | 21.99% | 4.02% | |||
2019-10-14 | TextSnake | 6.15% | 4.19% | 11.55% | 0.50% |