Combining on-line and off-line systems for handwriting recognition

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

Volumn 1, Issue , 2007, Pages 372-376

  Liwicki, Marcus ^a   Bunke, Horst ^a  

^a UNIVERSITY OF BERN   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER NETWORKS; LEARNING SYSTEMS;

DOCUMENT ANALYSIS; HANDWRITING RECOGNITION; INTERNATIONAL CONFERENCES;

HIDDEN MARKOV MODELS;

EID: 51149121436     PISSN: 15205363     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDAR.2007.4378734     Document Type: Conference Paper

References (18)

1. R. Bertolami, M. Zimmermann, and H. Bunke. Rejection strategies for offline handwritten text line recognition. Pattern Recognition Letters, 27(16):2005-2012, 2006.
2. H. Bunke. Recognition of cursive roman handwriting - past present and future. In Proc. 7th Int. Conf. on Document Analysis and Recognition, volume 1, pages 448-459, 2003.
3. A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of Royal Statistical Society B, 39(1):1-38, 1977.
4. G. A. Fink, M. Wienecke, and G. Sagerer. Video-based online handwriting recognition. In Proc. 6th Int. Conf. on Document Analysis and Recognition, pages 226-230, 2001.
5. J. Fiscus. A post-processing system to yield reduced word error rates: Recognizer output voting error reduction. In IEEE Workshop on Automatic Speech Recognition and Understanding, pages 347-352, 1997.
6. G. D. Forney. The Viterbi algorithm. In Proc. IEEE, volume 61, pages 268-278, 1973.
7. S. Günter and H. Bunke. HMM-based handwritten word recognition: on the optimization of the number of states, training iterations and Gaussian components. Pattern Recognition, 37:2069-2079, 2004.
8. S. Jaeger, S. Manke, J. Reichert, and A. Waibel. Online handwriting recognition: the NPen++ recognizer. Int. Journal on Document Analysis and Recognition, 3(3):169-180, 2001.
9. M. Liwicki and H. Bunke. IAM-OnDB - an on-line English sentence database acquired from handwritten text on a whiteboard. In Proc. 8th Int. Conf. on Document Analysis and Recognition, volume 2, pages 956-961, 2005.
10. M. Liwicki and H. Bunke. HMM-based on-line recognition of handwritten whiteboard notes. In Proc. 10th Int. Workshop on Frontiers in Handwriting Recognition, pages 595-599, 2006.
11. M. Liwicki and H. Bunke. Handwriting recognition of whiteboard notes - studying the influence of training set size and type. Int. Journal of Pattern Recognition and Artificial Intelligence, 21(1):83-98, 2007.
12. U.-V. Marti and H. Bunke. Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition system. Int. Journal of Pattern Recognition and Artificial Intelligence, 15:65-90, 2001.
13. M. Munich and P. Perona. Visual input for pen-based computers. In Proc. 3rd Int. Conf. on Pattern Recognition, pages 33-37, 1996.
14. R. Plamondon and S. N. Srihari. On-line and off-line handwriting recognition: A comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell., 22(1):63-84, 2000.
15. O. Velek, C.-L. Liu, S. Jaeger, and M. Nakagawa. An improved approach to generating realistic Kanji character images from on-line characters and its benefit to off-line recognition performance. In Proc. 16th Int. Conf. on Pattern Recognition, pages 588-591, 2002.
16. A. Vinciarelli. A survey on off-line cursive script recognition. Pattern Recognition, 35(7):1433-1446, 2002.
17. A. Viterbi. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. on Information Theory, 13:260-269, 1967.
18. L. Wang and T. Jiang. On the complexity of multiple sequence alignment. Journal of Computational Biology, 1(4):337-348, 1994.