- Task 1 - Text Localization
- Task 2 - Script identification
- Task 3 - Joint text detection and script identification
- Task 4 - End-to-End text detection and recognition
method: Sogou_OCR2019-11-11
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: Tencent-DPPR Team2019-06-04
Authors: Longhuang Wu, Shangxuan Tian, Chang Liu, Wenjie Cai, Jiachen Li, Sicong Liu, Haoxi Li, Chunchao Guo, Hongfa Wang, Hongkai Chen, Qinglin lu, Chun Yang, Xucheng Yin, Lei Xiao
Description: We are Tencent-DPPR (Data Platform Precision Recommendation) team. Our method follows the framework of Mask R-CNN that employs mask to detect multi-oriented scene texts. We use the MLT-19 and the MSRA-TD500 dataset to train our text detector, and we also apply a multi-scale training approach during training. To obtain the final ensemble results, we combined two different backbones and different multi-scale testing approaches.
method: Tencent-DPPR Team (Method_v0.3)2019-06-04
Authors: Longhuang Wu, Shangxuan Tian, Chang Liu, Wenjie Cai, Jiachen Li, Sicong Liu, Haoxi Li, Chunchao Guo, Hongfa Wang, Hongkai Chen, Qinglin lu, Chun Yang, Xucheng Yin, Lei Xiao
Description: We are Tencent-DPPR (Data Platform Precision Recommendation) team. Our method follows the framework of Mask R-CNN that employs mask to detect multi-oriented scene texts. We use the MLT-19 and the MSRA-TD500 dataset to train our text detector, and we also apply a multi-scale training approach during training. To obtain the final ensemble results, we combined two different backbones and different multi-scale testing approaches.
| Date | Method | Hmean | Precision | Recall | Average Precision | |||
|---|---|---|---|---|---|---|---|---|
| 2019-11-11 | Sogou_OCR | 83.72% | 89.60% | 78.57% | 76.23% | |||
| 2019-06-04 | Tencent-DPPR Team | 83.61% | 87.52% | 80.05% | 77.33% | |||
| 2019-06-04 | Tencent-DPPR Team (Method_v0.3) | 83.61% | 87.57% | 79.99% | 77.28% | |||
| 2019-06-04 | multi-stage_text_detector_v4 | 83.59% | 87.75% | 79.80% | 70.00% | |||
| 2019-06-03 | Tencent-DPPR Team (Method_v0.2) | 83.55% | 87.78% | 79.72% | 77.05% | |||
| 2019-06-04 | multi-stage_text_detector_v2 | 83.32% | 87.04% | 79.91% | 69.51% | |||
| 2019-06-04 | multi-stage_text_detector_v3 | 83.30% | 87.15% | 79.78% | 69.49% | |||
| 2019-06-03 | multi-stage_text_detector | 83.25% | 87.47% | 79.42% | 69.44% | |||
| 2019-06-03 | NJU-ImagineLab(v3) | 83.07% | 87.85% | 78.79% | 76.21% | |||
| 2019-05-30 | PMTD | 82.53% | 87.47% | 78.12% | 75.80% | |||
| 2019-05-27 | Tencent-DPPR Team (Method_v0.1) | 81.88% | 89.41% | 75.52% | 72.87% | |||
| 2019-05-29 | maskrcnn++ result | 80.35% | 82.64% | 78.19% | 64.64% | |||
| 2019-05-29 | IC_RL | 80.11% | 82.97% | 77.44% | 64.29% | |||
| 2019-06-02 | A two-stage text detector based on cascade rcnn(using total 10000 images of mlt19) | 78.38% | 82.26% | 74.85% | 71.27% | |||
| 2019-05-31 | A two-stage text detector based on cascade rcnn | 78.11% | 82.89% | 73.85% | 70.31% | |||
| 2019-06-03 | mm-maskrcnn_v2 | 76.79% | 84.73% | 70.21% | 67.44% | |||
| 2019-06-04 | TH-DL-v2 | 76.64% | 84.55% | 70.09% | 64.44% | |||
| 2019-06-03 | TH-DL-v1 | 76.59% | 84.51% | 70.03% | 64.35% | |||
| 2019-05-27 | TH-DL | 76.53% | 84.70% | 69.80% | 64.07% | |||
| 2019-05-26 | two stage text detector | 75.04% | 82.61% | 68.74% | 65.29% | |||
| 2019-06-03 | sot | 74.24% | 79.96% | 69.28% | 65.94% | |||
| 2019-06-02 | DISTILLED CRAFT | 72.94% | 81.22% | 66.19% | 59.16% | |||
| 2019-06-03 | text-mountain | 71.95% | 72.12% | 71.77% | 51.90% | |||
| 2019-06-03 | CRAFTS | 70.86% | 81.42% | 62.73% | 56.63% | |||
| 2019-06-04 | Unicamp-SRBR-MLT2019-PELEETEXT | 70.81% | 81.58% | 62.54% | 59.01% | |||
| 2019-06-03 | RRPN | 69.56% | 77.71% | 62.95% | 58.07% | |||
| 2019-06-04 | Unicamp-SRBR-MLT2019-FUSION-PSENET-PELEETEXT | 68.56% | 77.00% | 61.79% | 56.03% | |||
| 2019-05-28 | CRAFTS(Initial) | 68.11% | 79.51% | 59.56% | 54.50% | |||
| 2019-06-04 | Lomin OCR | 67.65% | 71.62% | 64.09% | 57.95% | |||
| 2019-06-03 | NXB OCR | 65.96% | 70.59% | 61.90% | 43.72% | |||
| 2019-05-24 | PSENet_v1 | 65.83% | 73.52% | 59.59% | 52.73% | |||
| 2019-05-27 | MLT2019 ETD | 64.36% | 78.71% | 54.44% | 42.93% | |||
| 2019-05-27 | CLTDR | 63.53% | 77.20% | 53.97% | 41.63% | |||
| 2019-05-27 | NXB OCR | 61.31% | 71.84% | 53.48% | 38.48% | |||
| 2019-06-03 | TP | 58.01% | 77.59% | 46.32% | 37.26% | |||
| 2019-05-28 | Unicamp-SRBR-MLT2019-S1 | 51.00% | 75.22% | 38.58% | 35.30% | |||
| 2019-06-04 | Cyberspace | 47.09% | 69.48% | 35.61% | 26.17% | |||
| 2019-05-28 | PydBox-TextDetector | 29.79% | 59.56% | 19.86% | 11.83% | |||
| 2019-05-05 | AAAA | 0.02% | 0.07% | 0.01% | 0.00% | |||
| 2019-05-27 | Unicamp-SRBR-MLT2019-S1 | 0.00% | 0.03% | 0.00% | 0.00% | |||
| 2019-06-01 | tsinghuaee51_MLT2019 | 0.00% | 0.04% | 0.00% | 0.00% | |||
| 2019-05-27 | 4Paradigm-Data-Intelligence | 0.00% | 0.00% | 0.00% | 0.00% |