Application of grammatical parsing to visual recognition tasks

Details Application of grammatical parsing to visual recognition tasks Application of grammatical parsing to visual recognition tasks

2005-06-13

2009-12-29

Strege, John B (Department: 2624)

Image analysis

Pattern recognition

Classification

C382S112000, C382S180000, C382S209000, C382S229000

Reexamination Certificate

active

07639881

ABSTRACT:
Image recognition is utilized to facilitate in scoring parse trees for two-dimensional recognition tasks. Trees and subtrees are rendered as images and then utilized to determine parsing scores. Other instances of the subject invention can incorporate additional features such as stroke curvature and/or nearby white space as rendered images as well. Geometric constraints can also be employed to increase performance of a parsing process, substantially improving parsing speed, some even resolvable in polynomial time. Additional performance enhancements can be achieved in yet other instances of the subject invention by employing constellations of integral images and/or integral images of document features.

REFERENCES:
patent: 5020112 (1991-05-01), Chou
patent: 2007/0055662 (2007-03-01), Edelman et al.
C. D. Manning, et al., Foundations of Statistical Natural Language Processing. The MIT Press, 1999.
Tobias Schefer, et al., Active Hidden Markov Models For Information Extraction. In Advances in Intelligent Data Analysis, 4th International Conference, IDA 2001, 2001.
P. Chou, “Recognition Of Equations Using A 2-D Stochastic Context-Free Grammar,” in SPIE Conference on Visual Communications and Image Processing, Philadelphia, PA, 1989.
M. Kay, “Algorithm Schemata And Data Structures In Syntactic Processing,” pp. 35-70, 1986.
Michael Shilman, et al., Spatial Recognition And Grouping Of Text And Graphics, Eurographics Workshop On Sketch-Based Interfaces And Modeling, 2004.
Michael Shilman, et al., Recognition And Grouping Of Handwritten Text In Diagrams And Equations, IWFHR 2004, Sep. 2004, Toyko, Japan.
Vinajak R. Borkar, et al., Automatically extracting structure from free text addresses, 2000, In Bulletin of the IEEE Computer Society Technical committee on Data Engineering. IEEE.
Remco Bouckaert, Low level information extraction: A bayesian network based approach, 2002, In Proc. TextML 2002, Sydney, Australia.
Claire Cardie, et al., Proposal for an interactive environment for information extraction, 1998, Technical Report TR98-1702, 2.
Rich Caruana, et al., High precision information extraction, Aug. 2000, In KDD-2000 Workshop on Text Mining.
M. Collins, Discriminative training methods for hidden nnarkov models : Theory and experiments with perceptron algorithms, 2002, In Proceedings of Empirical Methods in Natural Language Processing (EMNLP02).
Corinna Cortes, et al., Support-vector networks. Machine Learning, 1995, 20(3):273{297.
Y. Freund, et al., Large margin classification using the perceptron algorithm, Machine earning, 37(3):277{296.
Y. Freund, et al., Experiments with a new boosting algorithm, 1996, In International Conference on Machine Learning, pp. 148{156.
T. Kristjansson, et al., Interactive information extraction with constrained conditional random fields, 2004, In Proceedings of the 19th international conference on artificial intelligence, AAAI, pp. 412{418.
John Lafferty, et al., Conditional random fields: Probabilistic models for segmenting and labeling sequence data, 2001, In Proc. 18th International Conf. on Machine Learning, pp. 282{289. Morgan Kaufmann, San Francisco, CA.
M. Marcus, et al., The penn treebank: Annotating predicate argument structure, 1994.
Andrew McCallum, Efficiently inducing features of conditional random fields, 2003, In Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI03).
Andrew McCallum, et al., Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons, 2003, In Marti Hearst and Mari Ostendorf, editors, HLT-NAACL, Association for Computational Linguistics, Edmonton, Alberta, Canada.
Kamal Nigam, et al., Using maximum entropy for text classification, 1999, In IJCAI'99 Workshop on Information Filtering.
David Pinto, et al., Table extraction using conditional random fields, 2003, In Proceedings of the ACM SIGIR.
L.R. Rabiner, A tutorial on hidden markov models and selected applications in speech recognition, 1989, In Proc. of the IEEE, vol. 77, pp. 257{286.
Fei Sha, et al., Shallow parsing with conditional random fields. In Marti Hearst and Mari Ostendorf, editors, 2003, HLT-NAACL: Main Proceedings, pp. 213{220, Association for Computational Linguistics, Edmonton, Alberta, Canada.
J. Stylos, et al., Citrine:providing intelligent copy-and-paste, 2005, In Proceedings of ACM Symposium on User Interface Software and Technology (UIST 2004), pp. 185{188.
B. Tasker, et al., Max-margin parsing, 2004, In Empirical Methods in Natural Language Processing (EMNLP04).
S. Mao, et al., Document structure analysis algorithms: A literature survey, Jan. 2003, vol. 5010, pp. 197-207, In Proc. SPIE Electronic Imaging.
M. Krishnamoorthy, et al., Syntactic segmentation and labeling of digitized pages from technical journals, 1993, IEEE Transactions on Pattern Analysis and Machine Intelligence. vol. 15, pp. 737-747.
J. Kim, et al., Automated labeling in document images, Jan. 2001, In Document Recognition and Retrieval VIII, vol. 4307.
D. Niyogi, et al., Knowledge-based derivation of document logical structure, 1995, In Third International Conference on Document Analysis and Recognition, Montreal, Canada.
A. Conway, Page grammars and page parsing. A syntactic approach to document layout recognition, 1993, in Proceedings of the Second International Conference on Document Analysis and Recognition. Tsukuba Science City, pp. 761-764. Japan.
E. G. Miller, et al., Ambiguity and constraint in mathematical expression recognition, 1998, In Proceedings of the National Conference of Artificial Intelligence. American Association of Artificial Intelligence.
T. Tokuyasu, et al., Turbo recognition: a statistical approach to layout analysis, 2001, in Proceedings of the SPIE, pp. 123-129, vol. 4307, San Jose, CA.
T. Kanungo, et al., Stochastic language model for style-directed physical layout analysis of documents, 2003, In IEEE Transactions on Image Processing, vol. 5, No. 5.
D. Blostein, et al., Applying compiler techniques to diagram recognition, 2002, In Proceedings of the Sixteenth International Conference on Pattern Recognition, pp. 123-136, vol. 3, 2002.
J. F. Hull, Recognition of mathematics using a two dimensional trainable context-free grammar, Jun. 1996, Master's thesis, MIT.
N. Matsakis, Recognition of handwritten mathematical expressions, May 1999, Master's thesis, Massachusetts Institute of Technology, Cambridge, MA.
J. Lafferty, et al., Conditional random fields: Probabilistic models for segmenting and labeling sequence data, 2001, in Proc. 18th International Conf. on Machine Learning, pp. 282-289, Morgan Kaufmann, San Francisco, CA.
E. Charniak, et al., Edge-based best-first chart parsing, 1998, In Proceedings of the Fourteenth National Conference on Artificial Intelligence, pp. 127-133.
D. Klein, et al., A* parsing: Fast exact viterbi parse selection, 2001, Stanford University, Tech. Rep. dbpubs/2002-16.
Y. Freund, et al., A decision-theoretic generalization of on-line learning and an application to boosting, 1995, In Computational Learning Theory: Eurocolt '95, Springer-Verlag, pp. 23-37.
I. Philips, et al., Cd-rom document database standard, 1993, in Proceedings of 2nd International Conference on Document Analysis and Recognition.
P. Viola, et al., Rapid object detection using a boosted cascade of simple features, 2001, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
T. Breuel, High performance document layout analysis, in 2003 Symposium on Document Image Understanding Technology, Greenbelt Maryland.
R. Zanibbi, et al., A survey of table recognition: Models, observations, transformations, and inferences, 2004, International Journal of Document Analysis and Recognition, pp. 1-16, vol. 7, No. 1.
K.-F. Chan, et al., Mathematical expression recognition: a survey, 2000, International Journal on Document Analysis and Recognition, pp. 3-15, vol. 3.
E. Charniak, Statistical techniques for natural language parsing, 1997, AI Magazine.
M. Kay, Chart generation, 1996, in Proceedings of the 34th conference on Association for Computational

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