Overlapped text segmentation using markov random field and aggregation

ACM International Conference Proceeding Series

Volumn , Issue , 2010, Pages 129-134

  Peng, Xujun ^a   Setlur, Srirangaraj ^a   Govindaraju, Venu ^a   Sitaram, Ramachandrula ^b  

^a UNIVERSITY AT BUFFALO   (United States)

^b HEWLETT PACKARD LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AGGREGATION METHODS; DISTANCE METRICS; DOCUMENT ANALYSIS; FIXED SIZE; MARKOV RANDOM FIELD; MARKOV RANDOM FIELDS; PRINTED TEXTS; SHAPE CONTEXT FEATURES; TEXT SEGMENTATION; TRAINING SAMPLE;

EXTRACTION; FEATURE EXTRACTION;

ELECTRONIC WARFARE;

EID: 77954985065     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1815330.1815348     Document Type: Conference Paper

References (18)

1. S. Alpert, M. Galun, R. Basri, and A. Brandt. Image segmentation by probabilistic bottom-up aggregation and cue integration. Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on, pages 1-8, June 2007.
2. J. Banerjee, A. M. Namboodiri, and C. V. Jawahar. Contextual restoration of severely degraded document images. Proc. IEEE International Conference on CVPR, pages 517-524, 2009.
3. S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(4):509-522, April 2002.
4. H. Cao and V. Govindaraju. Handwritten carbon form preprocessing based on markov random field. Proc. IEEE Conference on Computer Vision and Pattern Recognition CVPR '07, pages 1-7, 2007.
5. J. J. Corso. Discriminative modeling by boosting on multilevel aggregates. Proc. IEEE Conference on Computer Vision and Pattern Recognition, pages 1-8, June 2008.
6. B. Eran and U. Shimon. Combined top-down/bottom-up segmentation. IEEE Journal Pattern Analysis and Machine Intelligence, 30(12):2109-2125, Dec 2008.
7. W. T. Freeman, T. R. Jones, and E. C. Pasztor. Example-based super-resolution. IEEE, Computer Graphics and Applications, 22(2):56-65, 2002.
8. W. T. Freeman and E. C. Pasztor. Learning low-level vision. International Journal of Computer Vision, 40(1):25-47, 2000.
9. J. Guo and M. Ma. Separating handwritten material from machine printed text using hidden markov models. Proc. IEEE 6th International Conference on Document Analysis and Recognition, pages 439-443, 2001.
10. G. Hamerly and C. Elkan. Learning the k in k-means. In Proc. 17th NIPS, 2003.
11. G. Nagy, S. Seth, and S. Stoddard. Document analysis with an expert system. Pattern Recognition in Practice II, pages 149-155, 1984.
12. L. O'Gorman. The document spectrum for page layout analysis. IEEE Trans. Pattern Analysis and Machine Intelligence, 15(11):1162-1173, Nov 1993.
13. J. Pearl. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, 1988.
14. X. Peng, S. Setlur, V. Govindaraju, R. Sitaram, and K. Bhuvanagiri. Markov random field based text identification from annotated machine printed documents. Proc. IEEE 10th International Conference on Document Analysis and Recognition(ICDAR 09), pages 431-435, 2009.
15. S. N. Srihari, V. Govindaraju, and A. Shekhawat. Interpretation of handwritten addresses in u. s. mailstream. Proc. IEEE 2nd International Conference on Document Analysis and Recognition, pages 291-294, 1993.
16. L. Xiong, F. Wang, and C. Zhang. Multilevel belief propagation for fast inference on markov random fields. Proc. Seventh IEEE International Conference on Data Mining(ICDM) 2007, pages 371-380, 28-31 Oct. 2007.
17. L. Zhao and L. S. Davis. Iterative figure-ground discrimination. Proceedings of the 17th International Conference on Pattern Recognition, 1:67-70, Aug 2004.
18. Y. Zheng, H. Li, and D. Doermann. Machine printed text and handwriting identification in noisy document images. IEEE Trans. Pattern Analysis and Machine Intelligence, 26(3):337-353, 2004.