Improving handwritten off-line text slant correction

Proceedings of the 6th IASTED International Conference on Visualization, Imaging, and Image Processing, VIIP 2006

Volumn , Issue , 2006, Pages 389-394

  Pastor, Moisés ^a   Toselli, Alejandro H ^b   Romero, Verónica ^b   Vidal, Enrique ^b  

^a UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

^b INSTITUTO TECNOLÓGICO DE INFORMÁTICA   (Spain)

Author keywords

Off line handwriting; Preprocess; Slant correction; Writing style

Indexed keywords

IMAGE PROCESSING; IMAGING SYSTEMS; VISUALIZATION;

OFF-LINE HANDWRITING; PREPROCESS; PROJECTION PROFILES; SLANT CORRECTION; SLANT CORRECTIONS; SOURCES OF VARIABILITIES; WRITING STYLE; WRITING STYLES;

DIGITAL IMAGE STORAGE;

EID: 56349090965     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. D. Y. an Gyeonghwan Kim. Slant correction of handwritten strings based on structural properties of korean characters. Pattern Recognition Letters, 12:2093-2101, 2003.
2. F. Jelinek. Statistical Methods for Speech Recognition. The MIT Press, Cambridge, Massachusetts London, England, 1998.
3. S. M. Katz. Estimation of Probabilities from Sparse Data for the Language Model Component of a Speech Recognizer. IEEE Trans. on Acoustics, Speech and Signal Processing, ASSP-35:400-401, Mar. 1987.
4. U. Marti and H. Bunke. A full english sentence database for off-line handwriting recognition. In In Proc. of the 5th Int. Conf. on Document Analysis and Recognition, pages 705-708, 1999.
5. U. Marti and H. Bunke. The iam-database: an english sentence database for off-line handwriting recognition. In International Journal on Document Analysis and Recognition, volume 5, pages 39-46, 2002.
6. M. Pastor, A. Toselli, and E. Vidal. Projection profi le based algorithm for slant removal. In International Conference on Image Analysis and Recognition (ICIAR'04), Lecture Notes in Computer Science, pages 183-190, Porto, Portugal, Sept. 2004. Springer-Verlag.
7. C. Sun and D. Si. Skew and slant correction for document images using gradient direction. In Proceedings of the 3th International Conference on Document Analysis and Recognition, pages 142-146, 1997.
8. A. H. Toselli, A. Juan, D. Keysers, J. Gonzlez, I. Salvador, H. Ney, E. Vidal, and F. Casacuberta. Integrated Handwriting Recognition and Interpretation using Finite-State Models. Int. Journal of Pattern Recognition and Artificial Intelligence, 18(4):519-539, June 2004.
9. A. H. Toselli, A. Juan, and E. Vidal. Spontaneous Handwriting Recognition and Classifi cation. In Proceedings of the 17th International Conference on Pattern Recognition, volume 1, pages 433-436, Cambridge, United Kingdom, Aug. 2004.
10. U.Marti and H.Bunke. Using a statistical language model to improve the performance of an hmm-based cursive handwriting recognition system. International Jounal of Pattern Recognition and Arificial Intelligence, 15(1):65-90, 2001.
11. A. Vinciarelli and J. Luettin. A new normalization technique for cursive handwritten words. Pattern Recognition Letters, 22(9):1043-1050, 2001.
12. I. H. Witten and T. C. Bell. The zero-frequency problem: Estimating the probabilities of novel events in adaptive text compression. IEEE Transactions on Information Theory, 17(4), 1991.
13. B. Yanikoglu and P. Sandon. Segmentation of off-line cursive handwriting using linear programming. Pattern Recognition Letters, 31:1825-1833, 1998.