Touching string segmentation using MRF

CIS 2009 - 2009 International Conference on Computational Intelligence and Security

Volumn 2, Issue , 2009, Pages 520-524

  Yang, Gang ^a   Yan, Ziye ^b   Zhao, Hong ^a  

^a HEBEI UNIVERSITY   (China)

^b BEIJING INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Belief propagation; Markov random field; Optical character recognition; Touching string segmentation

Indexed keywords

BELIEF PROPAGATION; EXAMPLE BASED; MARKOV RANDOM FIELD; MARKOV RANDOM FIELDS; MINIMIZATION METHODS; PROBABILITY MAPS; SEGMENTATION ALGORITHMS;

ARTIFICIAL INTELLIGENCE; OPTICAL CHARACTER RECOGNITION; STRUCTURAL FRAMES;

IMAGE SEGMENTATION;

EID: 77949308812     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIS.2009.171     Document Type: Conference Paper

References (17)

1. G. Richard, Casey and Eric Lecolinet, "A survey of methods and strategies in character segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 7, July, 1996, pp. 690-706.
2. G. Congedo, G. Dimauro, S. Impedovo and G. Pirlo, "Segmentation of numeric strings," Proc. IEEE Third International Conference on Document Analysis and Recognition, 1995, pp. 1038-1041.
3. U. Pal, A. Belad and C. Choisy, "Touching numeral segmentation using water reservoir concept," IEEE Transactions on Pattern Recognition Letters, vol. 24, 2003, pp. 261-272.
4. Zhongkan Lu, Zheru Chi, Wan Chi Siu and Pengfei Shi, "A background-thining-based approach for separating and recognizing connected handwritten digit strings," IEEE Transactions on Pattern Recognition, vol.132, 1999. pp. 921-933.
5. Y. K. Chen, J. F. Wang, "Segmentation of single- or multipletouching handwritten numeral string using background and foreground analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 22, no.11, pp. 1304-1317.
6. Rui Ma, Yingnan Zhao, Yongquan Xia and Yunyang Yan, "A Touching Pattern-oriented Strategy for Handwritten Digits segmentation," Proc. IEEE International Conference on Computational Intelligence and Security, vol.1, Dec. 2008, pp. 174-179.
7. K. K. Kim, J. H. Kim and C. Y. Suen, "Segmentation Based Recognition of Handwritten Touching Pairs of Digits Using Structural Features," IEEE Transactions on Pattern Recognition Letters, vol. 23, 2002, pp. 13-24.
8. C. Renaudin, Y. Ricquebourg and J. Camillerapp, "A General Method of Segmentation-Recognition Collaboration Applied to Pairs of Touching and Overlapping Symbols Renaudin," Proc. IEEE Ninth International Conference on Document Analysis and Recognition, vol. 2, Sept. 2007, pp. 659 - 663.
9. U. K. S. Jayarathna, G. E. M. D. C.Bandara, "A Junction Based Segmentation Algorithm for Offline Handwritten Connected Character Segmentation," Proc. IEEE International Conference on Intelligent Agents, Web Technologies and Internet Commerce, Nov. 2006, pp. 147 - 147.
10. M. Suwa, "Segmentation of connected handwritten numerals by graph representation," Proc. IEEE Eighth International Conference on Document Analysis and Recognition, vol. 2, Aug. 2005, pp.750 -754.
11. S. Z. li, "Markov random field modeling in computer vision," Spring-verlag, 2002.
12. R. Szeliski, R. Zabih, D. Scharstein and O. Veksler, V. Kolmogorov et al., "A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 6, June. 2008, pp. 1068-1080 .
13. Y. Boykov, O. Veksler and R. Zabih, "Fast approximate energy minimization via graph cuts," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 11, 2001, pp. 1222-1239.
14. J. Sun, N. N. Zheng and H. Y. Shum, "Stereo matching using belief propagation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 7, 2003, pp.787-800.
15. Y. Weiss and W.T. Freeman, "On the optimality of solutions of the max-product belief propagation algorithm in arbitrary graphs," IEEE Transactions on Information Theory, vol. 47, no. 2, 2001, pp. 723-735.
16. W. T. Freeman, E. C. P. Owen and T. Carmichael, "Learning Low-Level Vision," IEEE Transactions on Computer Vision, vol. 40, no.1, 2000, pp. 23-47.
17. S. Geman and D. Geman, "Stochastic Relaxation, Gibbs Distribution, and the Bayesian Restoration of Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 4, Nov. 1984, pp. 721-741.