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该数据集包含1000张图片,其中训练集500张,测试集500张,这些图片从谷歌街景中搜集。目标是多个方向,标注为word级别的,四个点的坐标。
算法 | 发表时间 | 算法类型 | P | R | F |
---|---|---|---|---|---|
CTPN2 | ECCV-2016 | Regression | 0.74 | 0.52 | 0.61 |
IncepText3 | IJCAI-2018 | Segmentation | 0.938 | 0.873 | 0.905 |
PSENet4 | CVPR-2019 | Segmentation | 0.8692 | 0.845 | 0.8569 |
CRAFT5 | CVPR-2019 | Segmentation | 0.898 | 0.843 | 0.869 |
CRAFTS6 | ECCV-2020 | Segmentation | 0.853 | 0.890 | 0.871 |
EAST7 | CVPR-2017 | Hybrid | 0.833 | 0.783 | 0.807 |
DB8 | AAAI-2020 | Hybrid | 0.918 | 0.832 | 0.873 |
ContourNet9 | CVPR2020 | Hybrid | 0.94 | 0.901 | 0.87 |
DRRG10 | CVPR2020 | GCN | 0.8853 | 0.8469 | 0.8656 |
TextFuseNet11 | IJCAI-PRICAI-20 | Hybrid | 0.940 | 0.906 | 0.922 |
SDM12 | ECCV-2020 | Segmentation | 0.9196 | 0.8922 | 0.9057 |
该数据集是曲形文本检测集,包含1000张训练集和500张测试集,文本使用14个边界点标注,行标注级别。
算法 | 发表时间 | 算法类型 | P | R | F |
---|---|---|---|---|---|
PSENet4 | CVPR-2019 | Segmentation | 0.848 | 0.797 | 0.822 |
CRAFT5 | CVPR-2019 | Segmentation | 0.86 | 0.811 | 0.835 |
DB8 | AAAI-2020 | Hybrid | 0.869 | 0.802 | 0.834 |
ContourNet9 | CVPR2020 | Hybrid | 0.857 | 0.84 | 0.848 |
DRRG10 | CVPR2020 | GCN | 0.8593 | 0.8302 | 0.8445 |
TextFuseNet11 | IJCAI-PRICAI-20 | Hybrid | 0.897 | 0.851 | 0.874 |
SDM12 | ECCV-2020 | Segmentation | 0.8840 | 0.8442 | 0.8636 |
与CTW1500不同的是,标注是word级别的,该数据集包含水平方向、多方向和曲形文本,共1225张训练集和300张测试集图片。
算法 | 发表时间 | 算法类型 | P | R | F |
---|---|---|---|---|---|
PSENet4 | CVPR-2019 | Segmentation | 0.84 | 0.779 | 0.809 |
CRAFT5 | CVPR-2019 | Segmentation | 0.876 | 0.799 | 0.836 |
CRAFTS6 | ECCV-2020 | Segmentation | 0.854 | 0.895 | 0.874 |
DB8 | AAAI-2020 | Hybrid | 0.871 | 0.825 | 0.847 |
ContourNet9 | CVPR2020 | Hybrid | 0.869 | 0.839 | 0.854 |
DRRG10 | CVPR2020 | GCN | 0.8654 | 0.8493 | 0.8573 |
TextFuseNet11 | IJCAI-PRICAI-20 | Hybrid | 0.892 | 0.858 | 0.875 |
SDM12 | ECCV-2020 | Segmentation | 0.9085 | 0.8603 | 0.8837 |
ICDAR2015[70]:D. Karatzas, L. Gomez-Bigorda, A. Nicolaou, S. K. Ghosh, A. D.Bagdanov, M. Iwamura, J. Matas, L. Neumann, V. R. Chandrasekhar, S. Lu, F. Shafait, S. Uchida, and E. Valveny. ICDAR 2015 competition on robust reading. In ICDAR, pages 1156–1160, 2015. Paper ↩︎
Tian Z, Huang W, He T, et al. Detecting text in natural image with connectionist text proposal network. European conference on computer vision(ECCV), 2016: 56-72. Paper Code ↩︎
Qiangpeng Yang, Mengli Cheng et al. IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection. In IJCAI 2018. Paper ↩︎
Wenhai W, Enze X, et al. Shape Robust Text Detection with Progressive Scale Expansion Network. In CVPR 2019. Paper Code ↩︎ ↩︎ ↩︎
Youngmin Baek, Bado Lee, et al. Character Region Awareness for Text Detection. In CVPR 2019. Paper ↩︎ ↩︎ ↩︎
Baek Y , Shin S , Baek J , et al. Character Region Attention For Text Spotting[J]. 2020. ↩︎ ↩︎
Zhou X, Yao C, Wen H, et al. EAST: an efficient and accurate scene text detector. CVPR, 2017: 2642-2651. Paper Code ↩︎
Minghui Liao, et al, Real-time Scene Text Detection with Differentiable Binarization. In AAAI, 2020. PaperCode ↩︎ ↩︎ ↩︎
Wang Y , Xie H , Zha Z , et al. ContourNet: Taking a Further Step toward Accurate Arbitrary-shaped Scene Text Detection[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. ↩︎ ↩︎ ↩︎
Zhang S X , Zhu X , Hou J B , et al. Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection[J]. 2020. ↩︎ ↩︎ ↩︎
Ye J , Chen Z , Liu J , et al. TextFuseNet: Scene Text Detection with Richer Fused Features[C]// Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20. 2020. ↩︎ ↩︎ ↩︎
Xiao S , Peng L , Yan R , et al. Sequential Deformation for Accurate Scene Text Detection[M]// Computer Vision – ECCV 2020. 2020. ↩︎ ↩︎ ↩︎
Yuliang L, Lianwen J, Shuaitao Z, et al. Curved Scene Text Detection via Transverse and Longitudinal Sequence Connection. Pattern Recognition, 2019.Paper ↩︎
Chee C K, Chan C S. Total-text: A comprehensive dataset for scene text detection and recognition.Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on. IEEE, 2017, 1: 935-942.Paper ↩︎
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