Image analysis – Learning systems – Neural networks
Patent
1993-04-29
1995-12-12
Boudreau, Leo H.
Image analysis
Learning systems
Neural networks
382181, G06K 966
Patent
active
054757681
ABSTRACT:
Pattern recognition, for instance optical character recognition, is achieved by training a neural network, scanning an image, segmenting the image to detect a pattern, preprocessing the detected pattern, and applying the preprocessed detected pattern to the trained neural network. The preprocessing includes determining a centroid of the pattern and centrally positioning the centroid in a frame containing the pattern. The training of the neural network includes randomly displacing template patterns within frames before applying the template patterns to the neural network.
REFERENCES:
patent: 3760356 (1973-09-01), Srivastava
patent: 3846752 (1974-11-01), Nakano et al.
patent: 3930231 (1975-12-01), Henrichon, Jr. et al.
patent: 3993976 (1976-11-01), Ginsburg
patent: 4225850 (1980-09-01), Chang et al.
patent: 4241329 (1980-12-01), Bahler et al.
patent: 4346405 (1982-08-01), Yoda et al.
patent: 4513441 (1985-04-01), Henshaw
patent: 4547800 (1985-10-01), Masaki
patent: 4817176 (1989-03-01), Marshall et al.
patent: 4958939 (1990-09-01), Samad
patent: 5121443 (1992-06-01), Tomlinson
patent: 5161203 (1992-11-01), Buckley
patent: 5245672 (1993-09-01), Wilson et al.
patent: 5263097 (1993-11-01), Katz et al.
patent: 5287272 (1994-02-01), Rutenberg et al.
patent: 5323471 (1994-06-01), Kayashi
Himes et al., "Centroid Calculation Using Neural Networks", Journal of Electronic Imaging, vol. 1(1), Jan. 1992, pp. 73-87.
Imai et al., "Pattern Extraction and Recognition for Noisy Images Using the Three-Layered BP Model", IEEE Int. Joint Conf on Neural Networks, vol. 1, Nov. 1991, pp. 262-267.
Seiler, "Small Object Counting with Cellular Neural Networks," 1990 IEEE Int. Workshop on Cellular Neural Networks and Their Applications, Dec. 1990, pp. 115-123.
You et al., "Connectionist Model for Object Recognition", Applications of Artificial Neural Networks III, SPIE, vol. 1709, Apr. 1992, pp. 200-207.
Elliman et al, "Shift Invariant Neural Net . . . ", IEEE Proc., vol. 137 Pt. I, No. 3, (Jun. 1990).
Gurgen et al, "On The Training Strategies For Neural Networks . . . ", Speech and Acoustics Lab, Tokyo, Japan.
Khotanzad, "Distortion Invariant Character Recognition by a Multi-Layer Perception", IEEE Int. Joint Conf on Neural Networks, Jul. 1988, pp. 625-632.
Gonzalez, Digital Image Processing, Addison-Wesley (1992). pp. 443-445 and 595-616.
J. Loncelle, et al., "Optical Character Recognition and Cooperating Neural Networks Techniques," Artificial Neural Networks, 2, I. Aleksander and J. Taylor, Ed., Elsevier Science Publishers, B.V., pp. 1591-1595, 1992.
A. Khotanzad and J. Lu, "Classification of Invariant Image Representations Using a Neural Network," 38 IEEE Transactions on Acoustics, Speech and Signal Processing 6, pp. 1028-1038, Jun. 1990.
S. N. Srihari, et al., "Pattern Recognition, Character Recognition and Optical Character Readers," Technical Report CEDAR-TR-91-1, Center for Document Analysis and Recognition, State University of New York at Buffalo, Buffalo, N.Y., pp. 1-51, May 1991.
Henry S. Baird, et al, "Reading Handwritten Digits: A Zip Code Recognition System," Computer, Jul. 1992, vol. 25, No. 7, IEEE Computer Society, pp. 59-63.
Avi-Itzhak Hadar I.
Diep Thanh A.
Garland Harry T.
Boudreau Leo H.
Canon Inc.
Enayati Elizabeth F.
Johns Andrew W.
Meyer Stuart P.
LandOfFree
High accuracy optical character recognition using neural network does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with High accuracy optical character recognition using neural network, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and High accuracy optical character recognition using neural network will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-1367146