The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
ICDAR 2013 Robust Reading Competition
Abstract
This report presents the final results of the ICDAR 2013 Robust Reading Competition. The competition is structured in three Challenges addressing text extraction in different application domains, namely born-digital images, real scene images and real-scene videos. The Challenges are organised around specific tasks covering text localisation, text segmentation and word recognition. The competition took place in the first quarter of 2013, and received a total of 42 submissions over the different tasks offered. This report describes the datasets and ground truth specification, details the performance evaluation protocols used and presents the final results along with a brief summary of the participating methods.
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Cites
Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics
Bernardin, K., & Stiefelhagen, R. (2008) · EURASIP Journal on Image and Video Processing · doi:10.1155/2008/246309 · Cited by 2,918
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
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
Speeding up Convolutional Neural Networks with Low Rank Expansions
Jaderberg, M., Vedaldi, A., & Zisserman, A. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.88 · Cited by 1,164
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
PixelLink: 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
Metadata
- Authors
- 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.
- Venue
- 2013 12th International Conference on Document Analysis and Recognition
- Publisher
- IEEE
- Published
- 2013-08
- Pages
- 1484-1493
- ISSN
- 1520-5363 (ISSN-L)
- OpenAlex
- W2008806374
- Cited by
- 1,360
- References
- 1
- FWCI
- 37.55
- Type
- conference paper
- Language
- en
- Open access
- closed
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- The paper
- 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). ICDAR 2013 Robust Reading Competition. 2013 12th International Conference on Document Analysis and Recognition. https://doi.org/10.1109/icdar.2013.221
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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.
