The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Symmetry-based text line detection in natural scenes
Abstract
Recently, a variety of real-world applications have triggered huge demand for techniques that can extract textual information from natural scenes. Therefore, scene text detection and recognition have become active research topics in computer vision. In this work, we investigate the problem of scene text detection from an alternative perspective and propose a novel algorithm for it. Different from traditional methods, which mainly make use of the properties of single characters or strokes, the proposed algorithm exploits the symmetry property of character groups and allows for direct extraction of text lines from natural images. The experiments on the latest ICDAR benchmarks demonstrate that the proposed algorithm achieves state-of-the-art performance. Moreover, compared to conventional approaches, the proposed algorithm shows stronger adaptability to texts in challenging scenarios.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139
Contour Detection and Hierarchical Image Segmentation
Arbeláez, P., Maire, M., Fowlkes, C., & Malik, J. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.161 · Cited by 5,454
(1989) · neural information processing systems · Cited by 3,736
Learning to detect natural image boundaries using local brightness, color, and texture cues
Martin, D. R., Fowlkes, C. C., & Malik, J. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1273918 · Cited by 2,420
The PASCAL visual object classes challenge 2006 (VOC2006) results
Everingham, M., Zisserman, A., Williams, C. K. I., & Gool, L. V. (2006) · Cited by 2,079
Detecting text in natural scenes with stroke width transform
Epshtein, B., Ofek, E., & Wexler, Y. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5540041 · Cited by 1,492
ICDAR 2013 Robust Reading Competition
Karatzas, D., Shafait, F., Uchida, S., Iwamura, M., Bigorda, L. G. I., Mestre, S. R., Mas, J., Mota, D. F., Almazan, J. A., & de las Heras, L. P. (2013) · 2013 12th International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2013.221 · Cited by 1,360
Cited by
Synthetic Data for Text Localisation in Natural Images
Gupta, A., Vedaldi, A., & Zisserman, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.254 · Cited by 1,563
Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes
Lyu, P., Liao, M., Yao, C., Wu, W., & Bai, X. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01264-9_5 · Cited by 522
Self-citationR2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
Jiang, Y., Zhu, X., Wang, X., Yang, S., Li, W., Wang, H., Fu, P., & Luo, Z. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.09579 · Cited by 448
Deep Direct Regression for Multi-oriented Scene Text Detection
He, W., Zhang, X.-Y., Yin, F., & Liu, C.-L. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.87 · Cited by 432
Multi-oriented Scene Text Detection via Corner Localization and Region Segmentation
Lyu, P., Yao, C., Wu, W., Yan, S., & Bai, X. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00788 · Cited by 389
Self-citation
Metadata
- Authors
- Zhang, Z., Shen, W., Yao, C., & Bai, X.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 2558-2567
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1935817682
- Cited by
- 278
- References
- 48
- FWCI
- 20.64
- Type
- conference paper
- Language
- en
- Open access
- closed
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