A discriminative semi-Markov model for robust scene text recognition

Proceedings - International Conference on Pattern Recognition

Volumn , Issue , 2008, Pages

  Weinman, Jerod J ^a   Learned Miller, Erik ^b   Hanson, Allen ^b  

^a GRINNELL COLLEGE   (United States)

^b Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; MARKOV PROCESSES;

BEAM SEARCH; BINARIZATIONS; LEXICON WORDS; LOW RESOLUTION IMAGES; SCENE TEXT; SEMI MARKOV MODEL; WAVELET FEATURES; WORD SEGMENTATION;

CHARACTER RECOGNITION;

EID: 77957964494     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/icpr.2008.4761818     Document Type: Conference Paper

References (15)

1. R. Beaufort and C. Mancas-Thillou. A weighted finite-state framework for correcting errors in natural scene OCR. Proc. ICDAR, 2:889-893, 2007.
2. X. Chen and A. L. Yuille. Detecting and reading text in natural scenes. In Proc. CVPR, pages 366-373, 2004.
3. C. Huang and S. N. Srihari. Word segmentation of offline handwritten documents. In Proc. Document Recognition and Retrieval, 2008.
4. X. Huang, A. Acero, and H.-W. Hon. Spoken Language Processing: A Guide to Theory, Algorithm, and System Development. Prentice Hall PTR, 2001.
5. C. Jacobs, P. Y. Simard, P. Viola, and J. Rinker. Text recognition of low-resolution document images. In Proc. ICDAR, pages 695-699, 2005.
6. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4):541-551, 1989.
7. R. Manmatha and J. L. Rothfeder. A scale space approach for automatically segmenting words from historical handwritten documents. IEEE Trans. PAMI, 27(8):1212-1225, 2005.
8. W. Niblack. An Introduction to Digital Image Processing. Prentice-Hall, Englewood-Cliffs, NJ, 1986.
9. J. Park, V. Govindaraju, and S. N. Srihari. Efficient word segmentation driven by unconstrained handwritten phrase recognition. In Proc. ICDAR, pages 605-608, 1999.
10. S. Sarawagi and W. W. Cohen. Semi-Markov conditional random fields for information extraction. In NIPS, pages 1185-1192.
11. E. P. Simoncelli and W. T. Freeman. The steerable pyramid: A flexible architecture for multi-scale derivative computation. In Proc. ICIP, pages 444-447, 1995.
12. C. Sutton and A. McCallum. Piecewise training of undirected models. In Proc. UAI, pages 568-575, 2005.
13. J. J. Weinman, A. R. Hanson, and E. Learned-Miller. Joint feature selection for object detection and recognition. Tech. Report UM-CS-2006-054, University of Massachusetts Amherst, Oct. 2006.
14. J. J. Weinman, E. Learned-Miller, and A. Hanson. Fast lexicon-based scene text recognition with sparse belief propagation. In Proc. ICDAR, pages 979-983, Sept 2007.
15. D. Zhang and S.-F. Chang. A Bayesian framework for fusing multiple word knowledge models in videotext recognition. In Proc. CVPR, volume 2, pages 528-533, 2003.