Character segmentation in highly blurred ancient printed documents

Proceedings - International Conference on Image Analysis and Processing, ICIAP 1999

Volumn , Issue , 1999, Pages 836-841

  Tonazzini, Anna ^a   Bedini, Luigi ^a  

^a ISTI CNR   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

CHARACTER BOUNDARIES; CHARACTER SEGMENTATION; DEGRADATION PROCESS; GEOMETRICAL CONSTRAINTS; IMAGE GRAY LEVEL; PRINTED DOCUMENTS; RADIOMETRIC CONSTRAINTS; SOLUTION STRATEGY;

CHARACTER RECOGNITION; IMAGE ANALYSIS; ITERATIVE METHODS;

IMAGE SEGMENTATION;

EID: 34249904608     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIAP.1999.797699     Document Type: Conference Paper

References (15)

1. E. Aarts and J. Korst. Simulated annealing and Boltzmann machines. John Wiley & Sons, New York, 1989.
2. H. I. Avi-Itzhak, T. A. Diep, and H. Garland. High accuracy optical character recognition using neural networks with centroid dithering. IEEE Trans. Patt. Anal. Machine Intell., 17(2):218-224, 1995.
3. G. R. Ayers and J. G. Dainty. Iterative blind deconvolution method and its applications. Opt. Lett., 13:547-549, 1988.
4. L. Bedini, A. Bozzi, and A. Tonazzini. Digital techniques for character recognition in old printed books and in modern damaged documents. 2nd Int. Congress on Science and Technology for the Safeguard of Cultural Heritage in the Mediterranean Basin (Paris, 5-9 July, 1999), 1999.
5. L. Bedini, I. Gerace, E. Salerno, and A. Tonazzini. Models and algorithms for edge-preserving image reconstruction. Advances in Imaging and Electron Physics, 97:86-189, 1996.
6. L. Bedini, A. Tonazzini, and S. Minutoli. Unsupervised edge-preserving image restoration via a saddle point approximation. Image and Vision Computing, to appear, 1999.
7. H. Derin and H. Elliott. Modeling and segmentation of noisy and textured images using Gibbs random fields. IEEE Trans. Patt. Anal. Machine Intell., 9(1):39-55, 1987.
8. S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Patt. Anal. Machine Intell., 6:721-740, 1984.
9. A. K. Katsaggelos and K. T. Lay. Maximum likelihood blur identification and image restoration using the EMalgorithm. IEEE Trans. Signal Proc., 39:729-732, 1991.
10. R. L. Lagendijk, A. M. Tekalp, and J. Biemond. Maximum likelihood image and blur identification: a unifying approach. Opt. Eng., 29:422-435, 1990.
11. S. Lakshmanan and H. Derin. Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing. IEEE Trans. Patt. Anal. Machine Intell., 11:799-813, 1989.
12. S. Z. Li. Markov Random Field Modeling in Computer Vision. Springer-Verlag, Tokyo, 1995.
13. A. Tonazzini and L. Bedini. Using intensity edges to improve parameter estimation in blind image restoration. In Proceedings of SPIE-Bayesian Inference for Inverse Problems, volume 3459, pages 73-81, San Diego, 1998.
14. Y. You and M. Kaveh. A regularization approach to joint blur identification and image restoration. IEEE Trans. Image Proc., 5:416-428, 1996.
15. J. Zhang. The mean field theory in EM procedures for blind Markov Random Field image restoration. IEEE Trans. Image Proc., 2:27-40, 1993.