method: CPN (multi-scale)2024-05-30
Authors: Longhuang Wu, Shangxuan Tian, Youxin Wang, Pengfei Xiong
Email: wlonghuang@gmail.com
Description: We propose a Complementary Proposal Network (CPN) that seamlessly and parallelly integrates semantic and geometric information for superior performance. This Result is achieved with single Swin-L backbone and multi-scale testing policy. No model ensemble is used.
method: AntFin-Cascade Mask R-CNN2023-02-23
Authors: Yangkun Lin, Tao Xu
Affiliation: Ant Group
Description: Our detector is based on Cascade Mask R-CNN. We use ConvNeXt-B as backbone. SynthText800k and
VISD10k are used to pretrain, and then we finetune on ArT, ICDAR2019-MLT and part of LSVT with multi-scale training. Multi-scale testing is used to get the result.
method: I3CL2021-07-05
Authors: Jian Ye, Jing Zhang, Juhua Liu, Bo Du and Dacheng Tao
Affiliation: Wuhan University SigmaLab, JD Explore Academy
Email: leaf-yej@whu.edu.cn
Description: A arbitrary-shaped scene text detector based on Mask R-CNN. In this result, we use ResNeSt-101 as the backbone. Multi-scale training and testing are applied to get the final result. Our training datasets contain SynthText (pretrain), ArT, ICDAR2019-MLT, and part of LSVT.
| Date | Method | Recall | Precision | Hmean | |||
|---|---|---|---|---|---|---|---|
| 2024-05-30 | CPN (multi-scale) | 88.35% | 83.51% | 85.86% | |||
| 2023-02-23 | AntFin-Cascade Mask R-CNN | 83.36% | 87.08% | 85.18% | |||
| 2021-07-05 | I3CL | 81.03% | 87.26% | 84.03% | |||
| 2020-05-21 | DuXiaoman_OCR | 79.35% | 87.81% | 83.36% | |||
| 2019-12-17 | Tencent TEG OCR | 81.16% | 85.64% | 83.34% | |||
| 2019-11-04 | Sogou_OCR | 78.49% | 87.94% | 82.95% | |||
| 2019-04-30 | MEGVII_Detection | 76.68% | 89.64% | 82.65% | |||
| 2020-04-22 | Mask R-CNN | 78.55% | 86.43% | 82.30% | |||
| 2022-04-19 | TextBPN++(ResNet-50 with DCN) | 77.05% | 84.48% | 80.59% | |||
| 2019-05-01 | NJU-ImagineLab | 74.21% | 87.35% | 80.24% | |||
| 2019-04-29 | ArtDet-v2 | 73.54% | 86.45% | 79.48% | |||
| 2023-08-09 | SRFormer (ResNet50-#1seg) | 73.51% | 86.08% | 79.30% | |||
| 2022-10-31 | TD-PPIoU (Long-Pretrain) | 74.21% | 85.06% | 79.27% | |||
| 2023-07-08 | CPNText-DETR(resnet-50) | 75.59% | 83.06% | 79.15% | |||
| 2024-01-18 | LRANet | 74.51% | 84.06% | 79.00% | |||
| 2022-04-21 | I3CL(ViTAEv2-S) | 75.42% | 82.82% | 78.95% | |||
| 2023-03-08 | TD-PPIoU | 76.96% | 81.00% | 78.93% | |||
| 2019-04-26 | baseline_polygon | 75.38% | 82.51% | 78.79% | |||
| 2020-10-01 | TextFuseNet (ResNeXt-101) | 72.77% | 85.42% | 78.59% | |||
| 2019-04-30 | CUTeOCR | 71.56% | 86.57% | 78.36% | |||
| 2022-07-11 | DPText-DETR (ResNet-50) | 73.70% | 82.97% | 78.06% | |||
| 2023-11-29 | ESRNet | 72.61% | 82.94% | 77.44% | |||
| 2019-04-29 | Sg_ptd | 70.41% | 85.98% | 77.42% | |||
| 2019-04-28 | Alibaba-PAI | 73.25% | 79.18% | 76.10% | |||
| 2022-03-25 | TextBPN++(ResNet-50) | 71.07% | 81.14% | 75.77% | |||
| 2021-03-26 | TextFuseNet (ResNet-50) | 69.42% | 82.59% | 75.44% | |||
| 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% | |||
| 2021-04-28 | NN_Chinese_and_euro6 | 66.51% | 82.74% | 73.74% | |||
| 2019-04-30 | TEXT_SNIPER | 71.45% | 76.17% | 73.74% | |||
| 2023-05-15 | dp_pq_nn | 66.27% | 82.60% | 73.54% | |||
| 2019-04-28 | CLTDR | 65.92% | 82.58% | 73.32% | |||
| 2021-04-08 | AutoCV | 69.59% | 77.25% | 73.22% | |||
| 2019-04-29 | CRAFT | 68.93% | 77.25% | 72.85% | |||
| 2019-04-30 | MaskRCNN_Text | 67.28% | 79.06% | 72.69% | |||
| 2023-05-15 | dp_nn | 67.30% | 78.92% | 72.65% | |||
| 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% | |||
| 2021-04-23 | HOCRA | 64.35% | 69.75% | 66.94% | |||
| 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% | |||
| 2021-04-28 | NN_euro6 | 51.76% | 85.50% | 64.48% | |||
| 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% | |||
| 2021-04-29 | HOCRA_base | 33.63% | 83.00% | 47.87% | |||
| 2019-04-26 | RAST: Robust Arbitrary Shape Text Detector | 35.44% | 71.08% | 47.30% | |||
| 2021-03-31 | inception baseline | 27.68% | 54.89% | 36.80% | |||
| 2019-04-30 | MSR | 0.46% | 0.55% | 0.50% |