The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Detecting and reading text in natural scenes
Abstract
This paper gives an algorithm for detecting and reading text in natural images. The algorithm is intended for use by blind and visually impaired subjects walking through city scenes. We first obtain a dataset of city images taken by blind and normally sighted subjects. From this dataset, we manually label and extract the text regions. Next we perform statistical analysis of the text regions to determine which image features are reliable indicators of text and have low entropy (i.e. feature response is similar for all text images). We obtain weak classifiers by using joint probabilities for feature responses on and off text. These weak classifiers are used as input to an AdaBoost machine learning algorithm to train a strong classifier. In practice, we trained a cascade with 4 strong classifiers containing 79 features. An adaptive binarization and extension algorithm is applied to those regions selected by the cascade classifier. Commercial OCR software is used to read the text or reject it as a non-text region. The overall algorithm has a success rate of over 90% (evaluated by complete detection and reading of the text) on the test set and the unread text is typically small and distant from the viewer.
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Cites
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
Neural network-based face detection
Rowley, H. A., Baluja, S., & Kanade, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655647 · Cited by 3,554
Cited by
Reading text in the wild with convolutional neural networks
Jaderberg, M., Simonyan, K., Vedaldi, A., & Zisserman, A. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-015-0823-z · Cited by 1,191
Scene Text Recognition using Higher Order Language Priors
Mishra, A., Alahari, K., & Jawahar, C. (2012) · Procedings of the British Machine Vision Conference 2012 · doi:10.5244/c.26.127 · Cited by 831
Deep Features for Text Spotting
Jaderberg, M., Vedaldi, A., & Zisserman, A. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10593-2_34 · Cited by 653
FOTS: Fast Oriented Text Spotting with a Unified Network
Liu, X., Liang, D., Yan, S., Chen, D., Qiao, Y., & Yan, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00595 · Cited by 554
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
- Chen, X., & Yuille, A. L.
- Venue
- Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.
- Publisher
- IEEE
- Published
- 2004-11-12
- Volume
- 2
- Pages
- 366-373
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2131673214
- Cited by
- 625
- References
- 3
- FWCI
- 11.77
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chen, X., & Yuille, A. L. (2004). Detecting and reading text in natural scenes. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. https://doi.org/10.1109/cvpr.2004.1315187
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
