Deep structured output learning for unconstrained text recognition

3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings

Volumn , Issue , 2015, Pages

  Jaderberg, Max ^a,b   Simonyan, Karen ^a,b   Vedaldi, Andrea ^a   Zisserman, Andrew ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b DEEPMIND   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CHARACTER RECOGNITION; COMPUTATIONAL LINGUISTICS; NEURAL NETWORKS; RANDOM PROCESSES; VISUAL LANGUAGES;

CONDITIONAL RANDOM FIELD; CONVOLUTIONAL NEURAL NETWORK; HIGHER ORDER TERMS; MULTITASK LEARNING; NUMERIC STRINGS; STATE OF THE ART; TEXT RECOGNITION; VISUAL LANGUAGE MODEL;

DEEP LEARNING;

EID: 85083954541     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Almazán, J., Gordo, A., Fornés, A., and Valveny, E. Word spotting and recognition with embedded attributes. In TPAMI, 2014.
2. Alsharif, O. and Pineau, J. End-to-End Text Recognition with Hybrid HMM Maxout Models. In Proc. ICLR, 2014.
3. Bissacco, A., Cummins, M., Netzer, Y., and Neven, H. PhotoOCR: Reading text in uncontrolled conditions. In Proc. ICCV, 2013.
4. Goel, V., Mishra, A., Alahari, K., and Jawahar, C. V. Whole is greater than sum of parts: Recognizing scene text words. In ICDAR, pp. 398–402, 2013.
5. Goodfellow, I. J., Bulatov, Y., Ibarz, J., Arnoud, S., and Shet, V. Multi-digit number recognition from street view imagery using deep convolutional neural networks. arXiv:1312.6082, 2013.
6. Gordo, A. Supervised mid-level features for word image representation. ArXiv e-prints, Oct 2014.
7. Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. CoRR, abs/1207.0580, 2012.
8. Jaderberg, M., Simonyan, K., Vedaldi, A., and Zisserman, A. Synthetic data and artificial neural networks for natural scene text recognition. NIPS Deep Learning Workshop 2014, 2014a.
9. Jaderberg, M., Simonyan, K., Vedaldi, A., and Zisserman, A. Reading text in the wild with convolutional neural networks. arXiv pre-print, 2014b.
10. Jaderberg, M., Vedaldi, A, and Zisserman, A. Deep features for text spotting. In European Conference on Computer Vision, 2014c.
11. Karatzas, D., Shafait, F., Uchida, S., Iwamura, M., Mestre, S. R., i Bigorda, L. G., Mas, J., Mota, D. F., Almazan, J., and de las Heras, L. P. ICDAR 2013 robust reading competition. In ICDAR, pp. 1484–1493, 2013.
12. LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998.
13. Lucas, S. M., Panaretos, A., Sosa, L., Tang, A., Wong, S., and Young, R. ICDAR 2003 robust reading competitions. In ICDAR, pp. 682–687, 2003.
14. Mishra, A., Alahari, K., and Jawahar, C. Scene text recognition using higher order language priors. 2012.
15. Novikova, T., Barinova, O., Kohli, P., and Lempitsky, V. Large-lexicon attribute-consistent text recognition in natural images. In Proc. ECCV, pp. 752–765. Springer, 2012.
16. Ozuysal, M., Fua, P., and Lepetit, V. Fast keypoint recognition in ten lines of code. In Proc. CVPR, 2007.
17. Perronnin, F., Liu, Y., Sánchez, J., and Poirier, H. Large-scale image retrieval with compressed fisher vectors. In Proc. CVPR, 2010.
18. Rodriguez-Serrano, J. A., Perronnin, F., and Meylan, F. Label embedding for text recognition. In Proc. BMVC., 2013.
19. Russel, Stuart, Norvig, Peter, et al. Artificial intelligence: A modern approach, 1995. Cited on, pp. 20, 1994.
20. Tompson, Jonathan J, Jain, Arjun, LeCun, Yann, and Bregler, Christoph. Joint training of a convolutional network and a graphical model for human pose estimation. In Advances in Neural Information Processing Systems, pp. 1799–1807, 2014.
21. Wang, K., Babenko, B., and Belongie, S. End-to-end scene text recognition. In Proc. ICCV, pp. 1457–1464. IEEE, 2011.
22. Wang, T., Wu, D. J, Coates, A., and Ng, A. Y. End-to-end text recognition with convolutional neural networks. In ICPR, pp. 3304–3308. IEEE, 2012.
23. Wickelgran, Wayne A. Context-sensitive coding, associative memory, and serial order in (speech) behavior. Psychological Review, 76(1):1, 1969.
24. Yao, Cong, Bai, Xiang, Shi, Baoguang, and Liu, Wenyu. Strokelets: A learned multi-scale representation for scene text recognition. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pp. 4042–4049. IEEE, 2014.