Markov random field based text identification from annotated machine printed documents

Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

Volumn , Issue , 2009, Pages 431-435

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

^a UNIVERSITY AT BUFFALO   (United States)

^b HEWLETT PACKARD LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BELIEF PROPAGATION; HANDWRITTEN TEXTS; IMBALANCED DATA; K-MEANS CLUSTERING ALGORITHM; MARKOV RANDOM FIELD; MARKOV RANDOM FIELDS; PRINTED DOCUMENTS; PRINTED TEXTS; RELABELING; TEXT IDENTIFICATION; WORD LEVEL;

BACKPROPAGATION ALGORITHMS; HIDDEN MARKOV MODELS;

CLUSTERING ALGORITHMS;

EID: 71249143497     PISSN: 15205363     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDAR.2009.237     Document Type: Conference Paper

References (16)

1. T. W. Anderson and D. A. Darling. Asymptotic theory of certain goodness-of-fit criteria based on stochastic processes. Annals of Mathematical Statistics 1952, pages 193-212.
2. 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.
3. J. B. D. S. Eduardo, B. Dubuisson, and F. Bortolozzi. Characterizing and distinguishing text in bank cheque images. Proc. XV Brazilian Symposium on Computer Graphics and Image Processing, pages 203-209, 2002.
4. F. Farooq, K. Sridharan, and V. Govindaraju. Identifying handwritten text in mixed documents. Proc. 18th International Conference on Pattern Recognition(ICPR) 2006, 2:1142-1145, 2006.
5. W. Freeman, O. Carmichael, and E. Pasztor. Learning low-level vision. International Journal of Computer Vision, 40(1):25-47, 2000.
6. S. E. Grigorescu, N. Petkov, and P. Kruizinga. Comparison of texture features based on gabor filters. IEEE Trans. on Image Processing, 11(10):1160-1167, 2002.
7. J. Guo and M. Ma. Separating handwritten material from machine printed text using hidden markov models. Proc. Sixth International Conference on Document Analysis and Recognition, pages 439-443, 2001.
8. G. Hamerly and C. Elkan. Learning the k in k-means. In Proc. 17th NIPS, 2003.
9. S. I. Jang, S. H. Jeong, and Y.-S. Nam. Classification of machine-printed and handwritten addresses on korean mail piece images using geometric features. Proc. 17th International Conference on Pattern Recognition(ICPR) 2004, 2:383-386, 2004.
10. J. Pearl. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, 1988.
11. N. Petkov. Biologically motivated computationally intensive approaches to image pattern recognition. Future Generation Computer Systems, 11(4-5):451-465, 1995.
12. N. Petkov and P. Kruizinga. Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells. Biological Cybernetics, 76(2):83-96, 1997.
13. S. Shetty, H. Srinivasan, M. Beal, and S. Srihari. Segmentation and labeling of documents using conditional random fields. Proc. Document Recognition and Retrieval IV, Proceedings of SPIE, pages 6500U-1-11, 2007.
14. 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.
15. Y. Zheng, H. Li, and D. Doermann. Machine printed text and handwriting identification in noisy document images. IEEE J PAMI, 26(3):337-353, 2004.
16. G. Zhu, Y. Zheng, D. Doermann, and S. Jaeger. Multi-scale structural saliency for signature detection. Proc. IEEE Conference on Computer Vision and Pattern Recognition(CVPR) '07, pages 1-8, 2007.