Image analysis – Histogram processing – For setting a threshold
Patent
1995-04-28
1997-11-18
Davis, George B.
Image analysis
Histogram processing
For setting a threshold
395 23, 382157, 382187, G06T 140, G06K 900
Patent
active
056896204
ABSTRACT:
A technique for automatically training a set of character templates using unsegmented training samples uses as input a two-dimensional (2D) image of characters, called glyphs, as the source of training samples, a transcription associated with the 2D image as a source of labels for the glyph samples, and an explicit, formal 2D image source model that models as a grammar the structural and functional features of a set of 2D images that may be used as the source of training data. The input transcription may be a literal transcription associated with the 2D input image, or it may be nonliteral, for example containing logical structure tags for document formatting, such as found in markup languages. The technique uses spatial positioning information about the 2D image modeled by the 2D image source model and uses labels in the transcription to determine labeled glyph positions in the 2D image that identify locations of glyph samples. The character templates are produced using the input 2D image and the labeled glyph positions without assigning pixels to glyph samples prior to training. In one implementation, the 2D image source model is a regular grammar having the form of a finite state transition network, and the transcription is also represented as a finite state network. The two networks are merged to produce a transcription-image network, which is used to decode the input 2D image to produce labeled glyph positions that identify training data samples in the 2D image. In one implementation of the template construction process, a pixel scoring technique is used to produce character templates contemporaneously from blocks of training data samples aligned at glyph positions.
REFERENCES:
patent: 4599692 (1986-07-01), Tan et al.
patent: 4769716 (1988-09-01), Casey et al.
patent: 5020112 (1991-05-01), Chou
patent: 5237627 (1993-08-01), Johnson et al.
patent: 5303313 (1994-04-01), Mark et al.
patent: 5321773 (1994-06-01), Kopec et al.
patent: 5493688 (1996-02-01), Weingard
patent: 5526444 (1996-06-01), Kopec et al.
patent: 5539839 (1996-07-01), Bellegarda et al.
G. Kopec, "Least-Squares Font Metric Estimation from Images", in IEEE Transactions on Image Processing, Oct., 1993, pp. 510-519.
G. Kopec and P. Chou, "Document Image Decoding Using Markov Source Models." in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, No. 6, Jun. 1994, pp. 602-617.
P.A. Chou, "Recognition of Equations Using a Two-Dimensional Stochastic Context-Free Grammar," in SPIE, vol. 1199, Visual Communications and Image Processing IV, 1989, pp. 852-863.
Huang, Ariki and Jack, Hidden Markov Models for Speech Recognition Edinburgh University Press, 1990, chapters 2, 5 and 6, pp. 10-51; 136-166; and 167-185.
L. Rabiner and B. Juang, "An Introduction to Hidden Markov Models", in IEEE ASSP Magazine, Jan. 1986, at pp. 4-16.
L. Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition", in Proceedings of the IEEE, vol. 77, No. 2, Feb., 1989, at pp. 257-285.
H. S. Baird, "A Self-Correcting 100-Font Classifier," in SPIE vol. 2181 Document Recognition, 1994, pp. 106-115.
S. Kuo and O.E. Agazzi, "Keyword spotting in poorly printed documents using pseudo 2D hidden Markov models," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, No. 8, Aug., 1994, pp. 842-848.
C. Bose and S. Kuo, "Connected and degraded text recognition using a hidden Markov model," in Proceedings of the International Conference on Pattern Recognition, Netherlands, Sep. 1992, pp. 116-119.
E. Levin and R. Pieraccini, "Dynamic planar warping for optical character recognition," in Proceedings of the 1992 International Conference on Acoustics, Speech and Signal Processing (ICASSP), San Francisco, California, Mar. 23-26, 1992, pp. III-149-III-152.
C. Yen and S. Kuo, "Degraded document recognition using pseudo 2D hidden Markov models in gray-scale Images". Copy received from authors without obligation of confidentiality, upon general request by applicants for information about ongoing or new work in this field. Applicants have no knowledge as to whether subject matter in this paper has been published.
R. Rubenstein, Digital Typography: An Introduction to Type and Composition for Computer System Design, Addison-Wesley, 1988, pp. 115-121.
Adobe Systems, Inc. Postscript Language Reference Manual, Addison-Wesley, 1985, pp. 95-96.
A. Kam and G. Kopec, "Separable source models for document image decoding", conference paper presented at IS&T/SPIE 1995 Intl. Symposium on Electronic Imaging, San Jose, CA, Feb. 5-10, 1995.
A. Kam, "Heuristic Document Image Decoding Using Separable Markov Models", S.M. Thesis, Massachusetts Institute of Technology, Cambridge, MA, Jun., 1993.
J. Coombs, A. Renear and S. DeRose, "Markup Systems and the Future of Scholarly Text Processing", Comm. of the ACM, vol. 30, No. 11, Nov., 1987, pp. 933-947.
P.A. Chou and G.E. Kopec, "A Stochastic Attribute Grammar Model of Document Production and Its Use in Document Image Decoding", conference paper presented at IS&T/SPIE 1995 Intl. Symposium on Electronic Imaging, San Jose, CA, Feb. 5-10, 1995.
D. E. Knuth, TEX and METAFONT: New Directions in Typesetting, Digital Press, 1979, Part II, pp. 41-50.
O. E. Agazzi et al., "Connected and Degraded Text Recognition Using Planar Hidden Markov Models," in Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP) 1993, Apr. 1993, pp. V-113-V-116.
Thomas M. Breuel, "A system for the off-line recognition of handwritten text" in Proceedings of the International Conference on Pattern Recognition (ICPR), Oct. 9-13, 1994, Jerusalem, Israel, pp. 129-134.
National Science Foundation (NSF) Grant Proposal for NSF Program Digital Libraries NSF 93-141 Feb. 4, 1994, submitted by the Regents of the University of California, Berkeley, document date Jan. 24, 1994, pp. i-xi, 2-5, 36-37, 101, and 106.
K. Y. Wong, R. G. Casey and F.M. Wahl, "Document Analysis System", IBM J Res Develop., vol. 26, No. 6, Nov. 1982, pp. 647-656.
Chou Philip Andrew
Kopec Gary E.
Niles Leslie T.
Bares Judith C.
Davis George B.
Xerox Corporation
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