Total-Text-Dataset (Official site)

Important Announcement

Total-Text and SCUT-CTW1500 are now part of the training set of the largest curved text dataset - ArT (Arbitrary-Shaped Text dataset). In order to retain the validity of future benchmarking on Total-Text datasets, the test-set images of Total-Text should be removed (with the corresponding ID provided HERE) from the ArT dataset shall one intend to leverage the extra training data from the ArT dataset. We count on the trust of the research community to perform such removal operation to attain the fairness of the benchmarking.

Table Ranking

  • The results from recent papers on Total-Text dataset are listed below where P=Precision, R=Recall & F=F-score.
  • If your result is missing or incorrect, please do not hesisate to contact us.
  • The baseline scores are based on our proposed [Poly-FRCNN-3] in this folder.
  • *Pascal VOC IoU metric; **Polygon Regression

Detection Leaderboard

Method Reported
on paper		 DetEval
(tp=0.4, tr=0.8)
(Default)		 DetEval
(tp=0.6, tr=0.7)
(New Proposal) 		Published at
P R F P R F P R F
Our Baseline [paper] 78.0 68.0 73.0 - - - 78.0 68.0 73.0 IJDAR2020
CRAFTS [paper] 89.5 85.4 87.4 - - - - - - ECCV2020
#ASTS_Weakly-ResNet101 (E2E) [paper] - - 87.3 - - - - - - TIP2020
TextFuseNet [paper] 89.0 85.3 87.1 - - - - - - IJCAI2020
#Boundary (E2E) [paper] 88.9 85.0 87.0 - - - - - - AAAI2020
PolyPRNet [paper] 88.1 85.3 86.7 - - - - - - ACCV2020
#Qin et al. (E2E) [paper] 87.8 85.0 86.4 - - - - - - ICCV2019
100%Poly [paper] 88.2 83.3 85.6 - - - - - - arXiv:2012
ContourNet [paper] 86.9 83.9 85.4 - - - - - - CVPR2020
#Text Perceptron (E2E) [paper] 88.8 81.8 85.2 - - - - - - AAAI2020
PAN-640 [paper] 89.3 81.0 85.0 - - - - - - ICCV2019
DB-ResNet50 (800) [paper] 87.1 82.5 84.7 - - - - - - AAAI2020
TextCohesion [paper] 88.1 81.4 84.6 - - - - - - arXiv:1904
Feng et al. [paper] 87.3 81.1 84.1 - - - - - - IJCV2020
ReLaText [paper] 84.8 83.1 84.0 - - - - - - arXiv:2003
CRAFT [paper] 87.6 79.9 83.6 - - - - - - CVPR2019
LOMO MS [paper] 87.6 79.3 83.3 - - - - - - CVPR2019
SPCNet [paper] 83.0 82.8 82.9 - - - - - - AAAI2019
#ABCNet (E2E) [paper] 85.4 80.1 82.7 - - - - - - CVPR2020
ICG [paper] 82.1 80.9 81.5 - - - - - - PR2019
FTSN [paper] *84.7 *78.0 *81.3 - - - - - - ICPR2018
PSENet-1s [paper] 84.02 77.96 80.87 - - - - - - CVPR2019
1TextField [paper] 81.2 79.9 80.6 76.1 75.1 75.6 83.0 82.0 82.5 TIP2019
#TextDragon (E2E) [paper] 85.6 75.7 80.3 - - - - - - ICCV2019
CSE [paper] 81.4
(**80.9)		 79.7
(**80.3)		 80.2
(**80.6)		 - - - - - - CVPR2019
MSR [paper] 85.2 73.0 78.6 82.7 68.3 74.9 81.4 72.5 76.7 arXiv:1901
ATTR [paper] 80.9 76.2 78.5 - - - - - - CVPR2019
TextSnake [paper] 82.7 74.5 78.4 - - - - - - ECCV2018
1CTD [paper] 74.0 71.0 73.0 60.7 58.8 59.8 76.5 73.8 75.2 PR2019
#TextNet (E2E) [paper] 68.2 59.5 63.5 - - - - - - ACCV2018
#,2Mask TextSpotter (E2E) [paper] 69.0 55.0 61.3 68.9 62.5 65.5 82.5 75.2 78.6 ECCV2018
CENet [paper] 59.9 54.4 57.0 - - - - - - ACCV2018
#Textboxes (E2E) [paper] 62.1 45.5 52.5 - - - - - - AAAI2017
EAST [paper] 50.0 36.2 42.0 - - - - - - CVPR2017
SegLink [paper] 30.3 23.8 26.7 - - - - - - CVPR2017

Note:

# Framework that does end-to-end training (i.e. detection + recognition).

1For the results of TextField and CTD, the improved versions of their original paper were used, and this explains why the performance is better.

2For Mask-TextSpotter, the relatively poor performance reported in their paper was due to a bug in the input reading module (which was fixed recently). The authors were informed about this issue.

End-to-end Recognition Leaderboard
(None refers to recognition without any lexicon; Full lexicon contains all words in test set.)

Method Backbone None (%) Full (%) FPS Published at
CRAFTS [paper] ResNet50-FPN 78.7 - - ECCV2020
MANGO [paper] ResNet50-FPN 72.9 83.6 4.3 AAAI2021
Text Perceptron [paper] ResNet50-FPN 69.7 78.3 - AAAI2020
ABCNet-MS [paper] ResNet50-FPN 69.5 78.4 6.9 CVPR2020
CharNet H-88 MS [paper] ResNet50-Hourglass57 69.2 - 1.2 ICCV2019
Qin et al. [paper] ResNet50-MSF 67.8 - - ICCV2019
ASTS_Weakly [paper] ResNet101-FPN 65.3 84.2 2.5 TIP2020
Boundary [paper] ResNet50-FPN 65.0 76.1 - AAAI2020
ABCNet [paper] ResNet50-FPN 64.2 75.7 17.9 CVPR2020
CAPNet [paper] ResNet50-FPN 62.7 - - ICASSP2020
Feng et al. [paper] VGG 55.8 79.2 - IJCV2020
TextNet [paper] ResNet50-SAM 54.0 - 2.7 ACCV2018
Mask TextSpotter [paper] ResNet50-FPN 52.9 71.8 4.8 ECCV2018
TextDragon [paper] VGG16 48.8 74.8 - ICCV2019
Textboxes [paper] ResNet50-FPN 36.3 48.9 1.4 AAAI2017

Description

In order to facilitate a new text detection research, we introduce Total-Text dataset (IJDAR)(ICDAR-17 paper) (presentation slides), which is more comprehensive than the existing text datasets. The Total-Text consists of 1555 images with more than 3 different text orientations: Horizontal, Multi-Oriented, and Curved, one of a kind.

Citation

If you find this dataset useful for your research, please cite

@article{CK2019,
  author    = {Chee Kheng Ch’ng and
               Chee Seng Chan and
               Chenglin Liu},
  title     = {Total-Text: Towards Orientation Robustness in Scene Text Detection},
  journal   = {International Journal on Document Analysis and Recognition (IJDAR)},
  volume    = {23},
  pages     = {31-52},
  year      = {2020},
  doi       = {10.1007/s10032-019-00334-z},
}

Feedback

Suggestions and opinions of this dataset (both positive and negative) are greatly welcome. Please contact the authors by sending email to
chngcheekheng at gmail.com or cs.chan at um.edu.my.

The project is open source under BSD-3 license (see the LICENSE file).

GitHub

GitHub - cs-chan/Total-Text-Dataset: Total Text Dataset. It consists of 1555 images with more than 3 different text orientations: Horizontal, Multi-Oriented, and Curved, one of a kind.
Total Text Dataset. It consists of 1555 images with more than 3 different text orientations: Horizontal, Multi-Oriented, and Curved, one of a kind. - GitHub - cs-chan/Total-Text-Dataset: Total Text...
GitHubcs-chan