method: MEGVII_Detection2019-04-30
Authors: Feng Wang, Li Hu, Peize Sun, Enze Xie, Wenhai Wang
Description: We proposed a method that use mask-rcnn to predict the segmentation as our boxes. Besides, we use senet-152 as our baseline and we also use multi-scale training and testing to improve the result.
method: NJU-ImagineLab2019-05-01
Authors: Xiaoge Song, Yao Xiao, Tong Lu
Description: It is a two-stage end-to-end detection framework, we use Mask R-CNN as our baseline and adapt it for arbitrary-shaped text.
ImagineLab, Nanjing University
method: ArtDet-v22019-04-29
Authors: Chao Ouyang, Xiang Chen, Lulu Xu
Description: An improved arbitrary-shaped text detection method.
Description Paper Source Code
| Date | Method | Recall | Precision | Hmean | |||
|---|---|---|---|---|---|---|---|
| 2019-04-30 | MEGVII_Detection | 76.68% | 89.64% | 82.65% | |||
| 2019-05-01 | NJU-ImagineLab | 74.21% | 87.35% | 80.24% | |||
| 2019-04-29 | ArtDet-v2 | 73.54% | 86.45% | 79.48% | |||
| 2019-04-26 | baseline_polygon | 75.38% | 82.51% | 78.79% | |||
| 2019-04-30 | CUTeOCR | 71.56% | 86.57% | 78.36% | |||
| 2019-04-29 | Sg_ptd | 70.41% | 85.98% | 77.42% | |||
| 2019-04-28 | Alibaba-PAI | 73.25% | 79.18% | 76.10% | |||
| 2019-04-30 | Fudan-Supremind Detection v3 | 71.61% | 79.26% | 75.24% | |||
| 2019-04-29 | SRCB_Art | 70.30% | 80.41% | 75.02% | |||
| 2019-04-30 | A scene text detection method based on maskrcnn | 66.25% | 85.69% | 74.72% | |||
| 2019-04-30 | DMText_art | 66.15% | 85.09% | 74.43% | |||
| 2019-04-30 | TEXT_SNIPER | 71.45% | 76.17% | 73.74% | |||
| 2019-04-28 | CLTDR | 65.92% | 82.58% | 73.32% | |||
| 2019-04-29 | CRAFT | 68.93% | 77.25% | 72.85% | |||
| 2019-04-30 | MaskRCNN_Text | 67.28% | 79.06% | 72.69% | |||
| 2019-04-30 | QAQ | 63.45% | 83.76% | 72.21% | |||
| 2019-04-30 | MaskDet | 67.04% | 76.47% | 71.44% | |||
| 2019-04-24 | fdu_ai | 61.61% | 82.11% | 70.40% | |||
| 2019-04-30 | CCISTD | 60.72% | 81.16% | 69.47% | |||
| 2019-04-30 | Mask RCNN | 73.20% | 65.16% | 68.95% | |||
| 2019-05-01 | TextMask_V1 | 70.58% | 67.33% | 68.92% | |||
| 2019-04-22 | MFTD: Mask Filters for Text Detection | 63.05% | 72.09% | 67.27% | |||
| 2019-04-25 | Art detect by vivo | 57.15% | 80.72% | 66.92% | |||
| 2019-04-29 | PAT-S.Y | 59.64% | 75.72% | 66.72% | |||
| 2019-04-16 | Art_test_baseline_task1 | 62.27% | 71.38% | 66.51% | |||
| 2019-04-30 | DMCA | 64.01% | 69.08% | 66.45% | |||
| 2019-04-30 | TMIS | 53.49% | 86.19% | 66.01% | |||
| 2019-04-22 | mask rcnn | 55.61% | 74.83% | 63.81% | |||
| 2019-05-01 | Unicamp-SRBR-PN-1 | 57.59% | 68.02% | 62.37% | |||
| 2019-04-26 | TP | 51.62% | 78.18% | 62.18% | |||
| 2019-04-28 | Improved Progressive scale expansion Net | 52.24% | 75.88% | 61.88% | |||
| 2019-04-23 | 1 | 59.04% | 57.38% | 58.20% | |||
| 2019-04-27 | TextCohesion_1 | 43.66% | 68.08% | 53.20% | |||
| 2019-04-30 | EM-DATA | 45.11% | 61.34% | 51.99% | |||
| 2019-04-26 | RAST: Robust Arbitrary Shape Text Detector | 35.44% | 71.08% | 47.30% | |||
| 2019-04-30 | MSR | 0.46% | 0.55% | 0.50% |