publication . Preprint . 2017

Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition

Chng, Chee Kheng; Chan, Chee Seng;
Open Access English
  • Published: 28 Oct 2017
Abstract
Comment: Accepted as Oral presentation in ICDAR2017 (Extended version, 13 pages 17 figures). We introduce a new scene text dataset namely as Total-Text, which is more comprehensive than the existing scene text datasets as it consists of 1555 natural images with more than 3 different text orientations, one of a kind
Subjects
ACM Computing Classification System: ComputingMethodologies_DOCUMENTANDTEXTPROCESSING
free text keywords: Computer Science - Computer Vision and Pattern Recognition
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25 references, page 1 of 2

[1] D. Karatzas, F. Shafait, S. Uchida, M. Iwamura, L. G. i Bigorda, S. R. Mestre, J. Mas, D. F. Mota, J. A. Almazan, and L. P. de las Heras, “Icdar 2013 robust reading competition,” in ICDAR, 2013.

[2] C. Yao, X. Bai, W. Liu, Y. Ma, and Z. Tu, “Detecting texts of arbitrary orientations in natural images,” in CVPR, 2012.

[3] Z. Zhang, W. Shen, C. Yao, and X. Bai, “Symmetry-based text line detection in natural scenes,” in CVPR, 2015.

[4] W. Huang, Z. Lin, J. Yang, and J. Wang, “Text localization in natural images using stroke feature transform and text covariance descriptors,” in ICCV, 2013.

[5] L. Neumann and J. Matas, “Scene text localization and recognition with oriented stroke detection,” in ICCV, 2013.

[6] W. Huang, Y. Qiao, and X. Tang, “Robust scene text detection with convolution neural network induced mser trees,” in ECCV, 2014.

[7] B. Shi, X. Bai, and S. Belongie, “Detecting oriented text in natural images by linking segments,” in CVPR, 2017.

[8] A. Risnumawan, P. Shivakumara, C. S. Chan, and C. L. Tan, “A robust arbitrary text detection system for natural scene images,” Expert Systems with Applications, vol. 41, no. 18, pp. 8027-8048, 2014.

[9] Z. Zhang, C. Zhang, W. Shen, C. Yao, W. Liu, and X. Bai, “Multi-oriented text detection with fully convolutional networks,” in CVPR, 2016.

[10] T. He, W. Huang, Y. Qiao, and J. Yao, “Accurate text localization in natural image with cascaded convolutional text network,” arXiv preprint arXiv:1603.09423, 2016.

[11] C. Yao, X. Bai, N. Sang, X. Zhou, S. Zhou, and Z. Cao, “Scene text detection via holistic, multi-channel prediction,” arXiv preprint arXiv:1606.09002, 2016.

[12] Q. Ye and D. Doermann, “Text detection and recognition in images and video : a survey,” T-PAMI, vol. 37, no. 7, pp. 1-20, 2014.

[13] D. Karatzas, L. Gomez-Bigorda, A. Nicolaou, S. Ghosh, A. Bagdanov, M. Iwamura, J. Matas, L. Neumann, V. R. Chandrasekhar, S. Lu et al., “Icdar 2015 competition on robust reading,” in ICDAR, 2015.

[14] V. Andreas, M. Tomas, N. Lukas, M. Jiri, and B. Serge, “Coco-text: Dataset and benchmark for text detection and recognition in natural images,” arXiv preprint arXiv:1601.07140, 2016.

[15] B. Epshtein, E. Ofek, and Y. Wexler, “Detecting text in natural scenes with stroke width transform,” in CVPR, 2010. [OpenAIRE]

25 references, page 1 of 2
Abstract
Comment: Accepted as Oral presentation in ICDAR2017 (Extended version, 13 pages 17 figures). We introduce a new scene text dataset namely as Total-Text, which is more comprehensive than the existing scene text datasets as it consists of 1555 natural images with more than 3 different text orientations, one of a kind
Subjects
ACM Computing Classification System: ComputingMethodologies_DOCUMENTANDTEXTPROCESSING
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Download from
25 references, page 1 of 2

[1] D. Karatzas, F. Shafait, S. Uchida, M. Iwamura, L. G. i Bigorda, S. R. Mestre, J. Mas, D. F. Mota, J. A. Almazan, and L. P. de las Heras, “Icdar 2013 robust reading competition,” in ICDAR, 2013.

[2] C. Yao, X. Bai, W. Liu, Y. Ma, and Z. Tu, “Detecting texts of arbitrary orientations in natural images,” in CVPR, 2012.

[3] Z. Zhang, W. Shen, C. Yao, and X. Bai, “Symmetry-based text line detection in natural scenes,” in CVPR, 2015.

[4] W. Huang, Z. Lin, J. Yang, and J. Wang, “Text localization in natural images using stroke feature transform and text covariance descriptors,” in ICCV, 2013.

[5] L. Neumann and J. Matas, “Scene text localization and recognition with oriented stroke detection,” in ICCV, 2013.

[6] W. Huang, Y. Qiao, and X. Tang, “Robust scene text detection with convolution neural network induced mser trees,” in ECCV, 2014.

[7] B. Shi, X. Bai, and S. Belongie, “Detecting oriented text in natural images by linking segments,” in CVPR, 2017.

[8] A. Risnumawan, P. Shivakumara, C. S. Chan, and C. L. Tan, “A robust arbitrary text detection system for natural scene images,” Expert Systems with Applications, vol. 41, no. 18, pp. 8027-8048, 2014.

[9] Z. Zhang, C. Zhang, W. Shen, C. Yao, W. Liu, and X. Bai, “Multi-oriented text detection with fully convolutional networks,” in CVPR, 2016.

[10] T. He, W. Huang, Y. Qiao, and J. Yao, “Accurate text localization in natural image with cascaded convolutional text network,” arXiv preprint arXiv:1603.09423, 2016.

[11] C. Yao, X. Bai, N. Sang, X. Zhou, S. Zhou, and Z. Cao, “Scene text detection via holistic, multi-channel prediction,” arXiv preprint arXiv:1606.09002, 2016.

[12] Q. Ye and D. Doermann, “Text detection and recognition in images and video : a survey,” T-PAMI, vol. 37, no. 7, pp. 1-20, 2014.

[13] D. Karatzas, L. Gomez-Bigorda, A. Nicolaou, S. Ghosh, A. Bagdanov, M. Iwamura, J. Matas, L. Neumann, V. R. Chandrasekhar, S. Lu et al., “Icdar 2015 competition on robust reading,” in ICDAR, 2015.

[14] V. Andreas, M. Tomas, N. Lukas, M. Jiri, and B. Serge, “Coco-text: Dataset and benchmark for text detection and recognition in natural images,” arXiv preprint arXiv:1601.07140, 2016.

[15] B. Epshtein, E. Ofek, and Y. Wexler, “Detecting text in natural scenes with stroke width transform,” in CVPR, 2010. [OpenAIRE]

25 references, page 1 of 2
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