Complex handwritten page segmentation using contextual models

Proceedings - Second International Conference on Document Image Analysis for Libraries, DIAL 2006

Volumn 2006, Issue , 2006, Pages 48-59

  Nicolas, Stéphane ^a   Paquet, Thierry ^a   Heutte, Laurent ^a  

^a UNIVERSITÉ DE ROUEN   (France)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE ANALYSIS; IMAGE SEGMENTATION; KNOWLEDGE REPRESENTATION; MARKOV PROCESSES; MATHEMATICAL MODELS; PROBLEM SOLVING; RANDOM PROCESSES;

CONTEXTUAL MODELS; DISCRIMATIVE MODELS; MARKOV RANDOM FIELD MODELS;

CONTEXT FREE LANGUAGES;

EID: 33750949045     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DIAL.2006.8     Document Type: Conference Paper

References (17)

1. H. Bunke, "Recognition of Cursive Roman Handwriting, Past, Present and Future", Proceeding of the seventh International Conference on Document Analysis and Recognition (ICDAR'03), pp. 448-459, Edinburgh, Scotland, 2003.
2. H. Baird, "Digital Libraries and Document Image Analysis", Proceeding of the seventh International Conference on Document Analysis and Recognition (ICDAR'03), pp. 2-14, Edinburgh, Scotland, 2003.
3. Nagy, G, "Twenty years of document image analysis in pamiTwenty Years", IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 22, no1, pp. 38-62, 2000.
4. Chellappa, R. et Jain, A., editors (1993). Markov Random Fields - Theory and application, Academic Press.
5. S. Geman, D. Geman, "Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images". IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 6, pp. 721-741, 1984.
6. S. Kirkpatrick, C. D. Gellatt and M. P. Vecchi, "Optimization by Simulated Annealing", Science (1983), no. 220, pp. 671-680, 1983.
7. J. E. Besag, "On the statistical analysis of dirty pictures", Journal of the Royal Statistical Society B, vol. 48, no. 3, pp. 259-302, 1986.
8. P. Chou, C. Brown, "The theory and practice of Bayesian image labeling", International Journal of Computer Vision, 4, pp. 185-210, 1990.
9. E. Geoffrois, "Multi-dimensional Dynamic Programming for statistical image segmentation and recognition", International Conference on Image and Signal Processing, pp. 397-403, Agadir, Morocco, 2003.
10. H. Derin, H. Elliott, "Modeling and segmentation of noisy and textured images using Gibbs random fields", IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 9, no1, pp. 39-55, 1987.
11. E. Geoffrois, S. Chevalier, F. Prêteux, "Programmation dynamique 2D pour la reconnaissance de caractères manuscrits par champs de Markov", proceedings of RFIA, Reconnaissance de Formes et Intelligence Artificielle, pp. 1143-1152, Toulouse, France, Janvier 2004.
12. S.Z. Li, Markov Random Field Modeling in Computer Vision, Springer, Tokyo, 1995.
13. E.Y. Kim, S.H. Park, H.J. Kim, "A genetic algorithm-based segmentation of Markov Random Field modeled images", IEEE Signal processing letters 11(7): 301-303, 2000.
14. S. Ouadfel, M. Batouche, "MRF-based image segmentation using Ant Colony System", in Electronic Letters on Computer Vision and Image Analysis (LCVIA), vol. 2, no1, pp. 12-24, August 2003.
15. "Performance Evaluation; Theory, Practice, and Impact", T. Kanungo, H. S. Baird, R.M. Haralick, Guest Editors, special issue of International Journal on Document Analysis and Recognition, vol. 4, no3, march 2002.
16. X. He, R.S. Zemel, M. A. Carreira-Perpinan, "Multiscale Conditional Random Fields for Image Labeling", IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04), pp. 695-702, Washington DC, USA, 2004.
17. J. Lafferty, A. McCallum, F. Pereira, "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data", 18th International Conference on Machine Learning, pp 282-289, Williamstown, USA, 2001.