barongeng / total-text-dataset

Total Text Dataset - ICDAR 2017. It consists of 1555 images with more than 3 different text orientations: Horizontal, Multi-Oriented, and Curved, one of a kind.

Total-Text-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.)

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 annotation tool 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

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. In addition, a recent ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (ArT), which is extended from Total-Text and SCUT-CTW1500, is held for stimulating more innovative ideas on the arbitrary-shaped text reading task. We welcome your participation, and the winner will walk away with substantial awards.

Table Ranking

• The results from recent papers on the 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.
• ^*Pascal VOC IoU metric; ^**Polygon Regression

Detection

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
CRAFT [paper]	87.6	79.9	83.6	-	-	-	-	-	-	CVPR2019
LOMO MS [paper]	87.6	79.3	83.3	-	-	-	-	-	-	CVPR2019
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
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:1901.02596
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 [paper]	68.2	59.5	63.5	-	-	-	-	-	-	ACCV2018
2Mask TextSpotter [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 [paper]	62.1	45.5	52.5	-	-	-	-	-	-	AAAI2017
EAST [paper]	50.0	36.2	42.0	-	-	-	-	-	-	CVPR2017
Baseline [paper]	33.0	40.0	36.0	-	-	-	-	-	-	ICDAR2017
SegLink [paper]	30.3	23.8	26.7	-	-	-	-	-	-	CVPR2017

Note:

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
(None refers to recognition without any lexicon; Full lexicon contains all words in test set.)

Method	None (%)	Full (%)	Published at
CNN+CTC [paper]	77.5	-	Neurocomputing2019
TextNet [paper]	54.0	-	ACCV2018
Mask TextSpotter [paper]	52.9	71.8	ECCV2018
Textboxes [paper]	36.3	48.9	AAAI2017

Description

In order to facilitate a new text detection research, we introduce Total-Text dataset (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

@inproceedings{CK2017,
  author    = {Chee Kheng Ch’ng and
               Chee Seng Chan},
  title     = {Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition},
  booktitle = {14th IAPR International Conference on Document Analysis and Recognition {ICDAR}},
  pages     = {935--942},
  year      = {2017},
  doi       = {10.1109/ICDAR.2017.157},
}

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 and Copyright

The project is open source under BSD-3 license (see the LICENSE file). Codes can be used freely only for academic purpose.

©2017-2019 Center of Image and Signal Processing, Faculty of Computer Science and Information Technology, University of Malaya.