The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Detecting texts of arbitrary orientations in natural images
Abstract
With the increasing popularity of practical vision systems and smart phones, text detection in natural scenes becomes a critical yet challenging task. Most existing methods have focused on detecting horizontal or near-horizontal texts. In this paper, we propose a system which detects texts of arbitrary orientations in natural images. Our algorithm is equipped with a two-level classification scheme and two sets of features specially designed for capturing both the intrinsic characteristics of texts. To better evaluate our algorithm and compare it with other competing algorithms, we generate a new dataset, which includes various texts in diverse real-world scenarios; we also propose a protocol for performance evaluation. Experiments on benchmark datasets and the proposed dataset demonstrate that our algorithm compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on texts of arbitrary orientations in complex natural scenes.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
A Computational Approach to Edge Detection
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The pascal visual object classes (VOC) challenge
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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
Shape matching and object recognition using shape contexts
Belongie, S., Malik, J., & Puzicha, J. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.993558 · Cited by 6,306
Cited by
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Xia, G.-S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., & Zhang, L. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00418 · Cited by 3,414
Self-citationGliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection
Xu, Y., Fu, M., Wang, Q., Wang, Y., Chen, K., Xia, G.-S., & Bai, X. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2974745 · Cited by 1,014
Self-citationPixelLink: Detecting Scene Text via Instance Segmentation
Deng, D., Liu, H., Li, X., & Cai, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12269 · Cited by 632
Efficient and Accurate Arbitrary-Shaped Text Detection With Pixel Aggregation Network
Wang, W., Xie, E., Song, X., Zang, Y., Wang, W., Lu, T., Yu, G., & Shen, C. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00853 · Cited by 632
A robust arbitrary text detection system for natural scene images
Risnumawan, A., Shivakumara, P., Chan, C. S., & Tan, C. L. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.07.008 · Cited by 531
Metadata
- Authors
- Yao, C., Bai, X., Liu, W., Ma, Y., & Tu, Z.
- Venue
- 2012 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 1083-1090
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1972065312
- Cited by
- 765
- References
- 5
- FWCI
- 29.40
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Yao, C., Bai, X., Liu, W., Ma, Y., & Tu, Z. (2012). Detecting texts of arbitrary orientations in natural images. 2012 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2012.6247787
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