Improving state-of-the-art OCR through high-precision document-specific modeling

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2010, Pages 1935-1942

  Kae, Andrew ^a   Huang, Gary ^a   Doersch, Carl ^b   Learned Miller, Erik ^a  

^a The Manning College of Information and Computer Sciences   (United States)

^b Carnegie Mellon University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPEARANCE MODELS; CHARACTER MODELS; DATA SETS; EMPIRICAL RESULTS; ERROR REDUCTION; HIGH CONFIDENCE; HIGH RESOLUTION; HIGH-PRECISION; HISTORICAL NEWSPAPERS; NOVEL METHODS; TESSERACT; TRAINING DATA; TRAINING SETS;

COMPUTER VISION; OPTICAL CHARACTER RECOGNITION; SECURITY OF DATA;

COMPUTATIONAL METHODS;

EID: 77955987982     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5539867     Document Type: Conference Paper

References (19)

1. T. Breuel. Character recognition by adaptive statistical similarity. In International Conference on Document Analysis and Recognition, 2003.
2. R. Casey. Text OCR by solving a cryptogram. In International Conference on Pattern Recognition, 1986.
3. J. Edwards and D. Forsyth. Searching for character models. In Neural Information Processing Systems, 2005.
4. T. K. Ho. Bootstrapping text recognition from stop words. In International Conference on Pattern Recognition, 1998.
5. T. K. Ho and G. Nagy. OCR with no shape training. In International Conference on Pattern Recognition, 2000.
6. J. Hobby and T. Ho. Enhancing degraded document images via bitmap clustering and averaging. In International Conference on Document Analysis and Recognition, 1997.
7. T. Hong and J. Hull. Character segmentation using visual inter-word constraints in a text page. In Proceedings of SPIE (International Society for Optics and Photonics), 1995.
8. T. Hong and J. Hull. Improving OCR performance with word image equivalence. In Symposium on Document Analysis and Information Retrieval, 1995.
9. T. Hong and J. J. Hull. Visual inter-word relations and their use in OCR post-processing. In International Conference on Document Analysis and Recognition, 1995.
10. A. Kae, G. Huang, and E. Learned-Miller. Bounding the probability of error for high precision recognition. Technical Report UM-CS-2009-031, University of Massachusetts Amherst, 2009.
11. A. Kae and E. Learned-Miller. Learning on the fly: Font free approaches to difficult OCR problems. In International Conference on Document Analysis and Recognition, 2009.
12. O. Kolak. A generative probabilistic ocr model for NLP applications. In North American Chapter of the Association for Computational Linguistics on Human Language Technology, 2003.
13. K. Kukich. Techniques for automatically correcting words in text. ACM Computing Surveys, 24(4):377-439, 1992.
14. D. Lee. Substitution deciphering based on HMMs with applications to compressed document processing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12), 2002.
15. D. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004.
16. G. Nagy. Efficient algorithms to decode substitution ciphers with applications to OCR. In International Conference on Pattern Recognition, 1986.
17. G. Nagy. Twenty years of document image analysis in PAMI. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 2000.
18. I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. In International Conference on Machine Learning, 2004.
19. M.Wick, M. Ross, and E. Learned-Miller. Context-sensitive error correction: Using topic models to improve OCR. In International Conference on Document Analysis and Recognition, 2007.