# Total-Text-Dataset (Official site) Updated on September 30, 2026 (Updated README.md & LICENSE. Added DATASET_TERMS.md & RIGHTS_NOTICE.md) Updated on April 06, 2022 (Detection leaderboard is updated with FCE, ABPNet, PCR, CentripetalText & HierText) Updated on April 29, 2020 (Detection leaderboard is updated - highlighted E2E methods. Thank you [shine-lcy](https://github.com/shine-lcy).) Updated on March 19, 2020 (Query on the new groundtruth of test set) Updated on Sept. 08, 2019 (New [training groundtruth](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Groundtruth/Text) of Total-Text is now available) Updated on Sept. 07, 2019 (Updated [Guided Annotation toolbox](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Annotation_tools) for scene text image annotation) Updated on Sept. 07, 2019 (Updated [baseline](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Baseline) as to our IJDAR) Updated on August 01, 2019 (Extended version with new baseline + annotation tool is accepted at [IJDAR](https://link.springer.com/article/10.1007/s10032-019-00334-z)) Updated on May 30, 2019 (Important announcement on Total-Text vs. ArT dataset) Updated on April 02, 2019 (Updated table ranking with default vs. our proposed DetEval) Updated on March 31, 2019 (Faster version DetEval.py, support Python3. Thank you [princewang1994](https://github.com/princewang1994).) Updated on March 14, 2019 (Updated table ranking with evaluation protocol info.) Updated on November 26, 2018 (Table ranking is included for reference.) Updated on August 24, 2018 (Newly added [Guided Annotation toolbox](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Annotation_tools) folder.) Updated on May 15, 2018 (Added groundtruth in '.txt' format.) Updated on May 14, 2018 (Added feature - 'Do not care' candidates filtering is now available in the latest python scripts.) Updated on April 03, 2018 (Added pixel level groundtruth) Updated on November 04, 2017 (Added text level groundtruth) Released on October 27, 2017 # News - We received some questions in regard to the new groundtruth for the test set of Total-Text. Here is an update. We do not release a new version of the test set groundtruth because 1) there is no need of standardising the length of the groundtruth vertices for testing purpose, it was proposed to facilitate training only, and 2) a new version of groundtruth would make the previous benchmarks irrelevant. Do contact us if you think there is a valid reason to require the new groundtruth for the test set, we shall discuss about it. - TOTAL-TEXT is a word-level based English curve text dataset. If you are interested in text-line based dataset with both English and Chinese instances, we highly recommend you to refer [SCUT-CTW1500](https://github.com/Yuliang-Liu/Curve-Text-Detector). In addition, a Robust Reading Challenge on Arbitrary-Shaped Text ([RRC-ArT](http://rrc.cvc.uab.es/?ch=14)), which is extended from Total-Text and SCUT-CTW1500, was held at ICDAR2019 to stimulate more innovative ideas on the arbitrary-shaped text reading task. Congratulations to all winners and challengers. The technical report of ArT can be found on at [this https URL](https://arxiv.org/abs/1909.07145). # 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)](http://rrc.cvc.uab.es/?ch=14). 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](https://github.com/cs-chan/Total-Text-Dataset/blob/master/Total_Text_ID_vs_ArT_ID.list)) 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](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Baseline). - *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
CentripetalText [paper] 90.67 85.19 87.85 - - - - - - NeurIPS2021
ABPNet [paper] 90.6 82.5 86.3 - - - - - - ICCV2021
CRAFTS [paper] 89.5 85.4 87.4 - - - - - - ECCV2020
FCE [paper] 89.3 82.5 85.8 - - - - - - CVPR2021
#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
PCR [paper] 88.5 82.0 85.2 - - - - - - CVPR2021
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
HierText [paper] 85.49 90.53 87.94 - - - - - - CVPR2022
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)](http://cs-chan.com/doc/IJDAR2019.pdf)[(ICDAR-17 paper)](https://arxiv.org/abs/1710.10400) [(presentation slides)](http://cs-chan.com/doc/TT_Slide.pdf), 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. ## Contributors: Chee Kheng Chng, Chun Chet Ng, Chee Seng Chan ## 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`. ## License, Dataset Terms and Copyright Total-Text contains different categories of material. Different terms apply to different components: | Material | Applicable terms | | --- | --- | | Software code, evaluation scripts, baseline code and annotation tools | BSD-3-Clause. See `LICENSE`. | | Total-Text annotations, ground-truth files, transcriptions, masks, metadata and dataset splits created for Total-Text | See `DATASET_TERMS.md`. | | Underlying images and other third-party material, if any | Not licensed under the BSD-3-Clause software licence. Separate rights may apply. | ## Academic research use The Total-Text dataset materials may be used for non-commercial academic research, education, benchmarking and scholarly publication subject to `DATASET_TERMS.md`. If a project is affiliated with or supported by a for-profit company but is intended solely for scholarly research and publication and is segregated from commercial product or model development, please obtain written confirmation from us before relying on the academic-use permission. ## Commercial use Commercial use of the Total-Text dataset materials requires prior written permission from the applicable rights holder. Commercial use includes, for example, use of the dataset to support product or service development, commercial model training or fine-tuning, internal commercial benchmarking or validation, deployment, or other business-facing R&D. For permission requests, contact: Dr. Chee Seng Chan at `cs.chan at um.edu.my`. ## Attribution Academic publications using Total-Text should cite: ```bibtex @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}, } @inproceedings{ch2017total, title={Total-text: A comprehensive dataset for scene text detection and recognition}, author={Ch'Ng, Chee Kheng and Chan, Chee Seng}, booktitle={2017 14th IAPR international conference on document analysis and recognition (ICDAR)}, volume={1}, pages={935--942}, year={2017}, organization={IEEE} } ``` ## Rights notice Permissions are granted only to the extent that the applicable licensor is authorised to grant them. Nothing in this repository grants rights in third-party material beyond the rights held by the applicable rights holder. These clarified terms apply prospectively from 01 Oct 2026. They do not purport to revoke or determine the scope of any rights that may have been validly granted under an earlier version of this repository. ©2017-2026 Chee Seng Chan. Developed at Universiti Malaya.