Unconstrained handwritten document layout extraction using 2D conditional random fields

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

Volumn , Issue , 2009, Pages 853-857

  Montreuil, Florent ^a   Grosicki, Emmanuèle ^a   Heutte, Laurent ^b   Nicolas, Stéphane ^b  

^a DGA/Centre d'Expertise Parisien   (France)

^b UNIVERSITÉ DE ROUEN   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CONDITIONAL PROBABILITIES; CONDITIONAL RANDOM FIELD; DOCUMENT IMAGES; DYNAMIC PROGRAMMING METHODS; HANDWRITTEN DOCUMENT; IMAGE FEATURES; KEYPOINTS; LAYOUT EXTRACTION; METHOD MODEL; NEW APPROACHES; PRIOR KNOWLEDGE; STOCHASTIC PROCESS;

CONTENT BASED RETRIEVAL; PIXELS; RANDOM PROCESSES; STOCHASTIC MODELS; SYSTEMS ENGINEERING;

LABELS;

EID: 71249147671     PISSN: 15205363     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDAR.2009.132     Document Type: Conference Paper

References (14)

1. C. C. Chang. Libsvm: a library for support vector machines. Software available at: http://www.csie.ntu.edu.tw, 2006.
2. E. Geoffrois. Multi-dimensional dynamic programming for statistical image segmentation and recognition. In the Conference on Image and Signal Processing (ICISP03), 2003.
3. E. Grosicki. Rimes evaluation campaign for handwritten mail processing. the 11th International Conference on Frontiers in Handwritting Recognition (ICFHR08), 2008.
4. X. He. Multiscale conditional random fields for image labelling. In the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR04), volume 2, pages 695-702, 2004.
5. S. Klink. Document structure analysis based on layout and textual features. In the InternationalWorkshop on Document Analysis Systems, DAS00, pages 99-111. IAPR, 2000.
6. S. Kumar. Discriminative random fields. International Journal of Computer Vision, 68(2):179-202, 2006.
7. J. D. Lafferty. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In the 18th International Conference on Machine Learning (ICML01), pages 282-289. Morgan Kaufmann, San Francisco, CA, 2001.
8. A. Lemaitre. Multiresolution cooperation makes easier document structure recognition. IJDAR, 11(2), November 2008.
9. M. Lemaitre. Approche markovienne bidimensionnelle d'analyse et de reconnaissance de documents manuscrits. Technical report, France, 2007.
10. V. Malathii. Support vector machine for discrimination between fault and magnetizing inrush current in power transformer. Journal of Computer Science, 11(3):894-897, 2008.
11. S. Nicolas. Handwritten document segmentation using hidden markov random fields. In the 8th International Conference on Document Analysis and Recognition (ICDAR05), pages 212-216, Washington, DC, USA, 2005. IEEE Computer Society.
12. S. Nicolas. 2d markovian models for document structure analysis. In the 11th International Conference on Frontiers in Handwriting Recognition (ICWFHR08), pages 658-663, Montreal, Quebec, Canada, 2008.
13. S. Souafi. Contribution a la reconnaissance des structures des documents ecrits : Approche probabilistes. Technical report, France, 2002.
14. Y. Y. Tang. Document analysis and understanding : A brief survey. In the 1st International Conference on Document Analysis and Recognition (ICDAR91), pages 17-31, 1991.