Boosted decision trees for word recognition in handwritten document retrieval

SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2005, Pages 377-383

  Howe, Nicholas R ^a   Rath, Toni M ^b   Manmatha, R ^b  

^a SMITH COLLEGE   (United States)

^b Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

adaboost; decision theory; handwriting retrieval; historical manuscripts

Indexed keywords

ADA BOOST CLASSIFIERS; BOOSTED DECISION TREES; HANDWRITING RETRIEVALS; HANDWRITTEN DOCUMENT; HISTORICAL MANUSCRIPTS; RETRIEVAL PERFORMANCE; SKEWED DISTRIBUTION; UNSOLVED PROBLEMS;

ADAPTIVE BOOSTING; COMPUTATIONAL LINGUISTICS; DECISION THEORY; DECISION TREES; FORESTRY; IMAGE CLASSIFICATION; INFORMATION RETRIEVAL; VOCABULARY CONTROL;

CHARACTER RECOGNITION;

EID: 84885650580     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1076034.1076099     Document Type: Conference Paper

References (28)

1. The lemur toolkit for language modeling and information retrieval, 2005. available at http://www-2.cs.cmu.edu/~lemur/.
2. V. N. Anh and A. Moffat. Robust and web retrieval with document-centric integral impacts. In Proc. 2003 Text Retrieval Conference, November 2003.
3. V. N. Anh and A. Moffat. Collection-independent document-centric impacts. In Proc. Australasian Document Computing Symposium, pages 25-32, 2004.
4. V. Athitsos, J. Alon, S. Sclaroff, and G. Kollios. Boostmap: A method for efficient approximate similarity rankings. In IEEE Conference on Computer Vision and Pattern Recognition, pages 268-275, 2004.
5. L. Breiman. Arcing classifiers. The Annals of Statistics, 26(3):801-849, 1998. (Pubitemid 128450035)
6. L. Fei-Fei, R. Fergus, and P. Perona. A bayesian approach to unsupervised one-shot learning of object categories. In International Conference on Computer Vision, pages 1134-1141, 2003.
7. Y. Freund and R. E. Schapire. Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning, pages 148-156, 1996.
8. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting. The Annals of Statistics, 28(2):337-407, 2000. (Pubitemid 33227445)
9. N. R. Howe. A closer look at boosted image retrieval. In International Conference on Image and Video Retrieval, pages 61-70, 2003.
10. G. Kim, V. Govindaraju, and S. N. Srihari. Architecture for handwritten text recognition systems. In S.-W. Lee, editor, Advances in Handwriting Recognition, pages 163-172. World Scientific, 1999.
11. J. Lafferty and C. Zhai. Document language models, query models, and risk minimization for information retrieva. In the 24th annual international ACM SIGIR conference, pages 111-119, 2001.
12. V. Lavrenko, T. Rath, and R. Manmatha. Holistic word recognition for handwritten historical documents. In Proc. of the IEEE Workshop on Document and Image Analysis for Libraries DIAL'04, pages 278-287, 2004.
13. R. Manmatha and W. B. Croft. Word spotting: Indexing handwritten manuscripts. In M. Maybury, editor, Intelligent Multi-media Information Retrieval, pages 43-64. AAAI/MIT Press, 1997.
14. R. Manmatha and N. Srimal. Scale space technique for word segmentation in handwritten manuscripts. In In the Proc. of the Second International Conference on Scale-Space Theories in Computer Vision (Scale-Space'99), pages 22-33, Sep. 1999.
15. U.-V. Marti and H. Bunke. Using a statistical language model to improve the performance of an hmm-based cursive handwriting recognition system. Int'l Journal of Pattern Recognition and Artifical Intelligence, 15(1):65-90, 2001. (Pubitemid 32272685)
16. D. Mease, A. J. Wyner, and A. Buja. Boosted classification trees and class probability/quantile estimation. Technical report, submitted to Journal of Machine Learning Research, 2004.
17. D. Metzler and R. Manmatha. An inference network approach to image retrieval. In Proc. International Conference on Image and Video Retrieval (CIVR-2004), pages 42-50, July 2004.
18. R. Plamondon and S. N. Srihari. On-line and off-line handwriting recognition: A comprehensive survey. IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(1):63-84, 2000. (Pubitemid 32132955)
19. J. Ponte and W. B. Croft. A language modeling approach to information retrieval. In the 21st annual international ACM SIGIR conference, pages 275-281, 1998.
20. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
21. T. Rath, V. Lavrenko, and R. Manmatha. A statistical approach to retrieving historical manuscripts without recognition. Technical report, Center for Intelligent Information Retrieval technical report MM-42, 2003.
22. T. Rath and R. Manmatha. Word image matching using dynamic time warping. In Proceedings of CVPR'03, volume 2, pages 521-527, 2003.
23. T. Rath, R. Manmatha, and V. Lavrenko. A search engine for historical manuscript images. In The 27th Annual international ACM SIGIR Conference, pages 369-376, 2004.
24. K. Tieu and P. Viola. Boosting image retrieval. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume I, pages 228-235, 2000.
25. V.Govindaraju and H. Xue. Fast handwriting recognition for indexing historical documents. In Proc. of the Int'l Workshop on Document Image Analysis for Libraries (DIAL), pages 314-320, 2004.
26. A. Vinciarelli, S. Bengio, and H. Bunke. Offline recognition of unconstrained handwritten texts using hmms and statistical language models. IEEE Transactions PAMI, 26(6):709-720, 2004.
27. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In IEEE Conference on Computer Vision and Pattern Recognition, pages 511-518, 2001.
28. G. Zipf. Human Behaviour and the Principle of Least-Effort. Addison-Wesley, Cambridge, MA, 1949.