The DOI matches its Crossref record; 154 of the 154 references it cites were found in a registry or an index.
Scene Text Detection and Recognition: The Deep Learning Era
Open accessAbstract
With the rise and development of deep learning, computer vision has been tremendously transformed and reshaped. As an important research area in computer vision, scene text detection and recognition has been inevitably influenced by this wave of revolution, consequentially entering the era of deep learning. In recent years, the community has witnessed substantial advancements in mindset, methodology and performance. This survey is aimed at summarizing and analyzing the major changes and significant progresses of scene text detection and recognition in the deep learning era. Through this article, we devote to: (1) introduce new insights and ideas; (2) highlight recent techniques and benchmarks; (3) look ahead into future trends. Specifically, we will emphasize the dramatic differences brought by deep learning and remaining grand challenges. We expect that this review paper would serve as a reference book for researchers in this field. Related resources are also collected in our Github repository (https://github.com/Jyouhou/SceneTextPapers).
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Cites
ImageNet classification with deep convolutional neural networks
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U-Net: Convolutional Networks for Biomedical Image Segmentation
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
You Only Look Once: Unified, Real-Time Object Detection
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Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Fully convolutional networks for semantic segmentation
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Histograms of Oriented Gradients for Human Detection
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Cited by
Text Recognition in the Wild: A Survey
Chen, X., Jin, L., Zhu, Y., Luo, C., & Wang, T. (2022) · ACM Computing Surveys · doi:10.1145/3440756 · Cited by 205
“ovide a comprehensive review of publicly available resources, including the standard benchmark datasets and related code. There are several STR reviews in the literature [26], [27], [28], [29], [30], [31]. However, most of the abovementioned surveys [26], [27], [28], [29], [30] are outdated. Many recent advances, such…”
Background
Metadata
- Authors
- Long, S., He, X., & Yao, C.
- Venue
- International Journal of Computer Vision
- Published
- 2020-08-27
- Volume
- 129
- Issue
- 1
- Pages
- 161-184
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W3082397598
- Cited by
- 527
- References
- 154
- FWCI
- 29.59
- Type
- journal article
- Language
- en
- Open access
- green
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