Abstract

A method for recognizing patterns comprising extracting features from a digital image, generating a numerical representation of each feature, indexing into a look-up table using the numerical representation to determine candidate pattern-types wherein the look-up table was generated by using a number of sample patterns to generate additional patterns based on relationships between the sample patterns; and selecting among the candidate pattern-types using selected contextual information. Extracting features from a digital image comprises identifying line segments in the digital image, grouping together adjacent line segments of the image that form features, storing the coordinates of the features to a file, the coordinates of the feature define a portion of the digital image containing the feature, dividing the portion of the digital image containing the feature into a number of cells, the number of cells is less than the number of pixels in each feature. Generating a numerical representation of each feature comprises generating a count of the number of pixels that are set in each cell, assigning a value to each cell based on whether the count for the cell exceeds a threshold, and indexing into a look-up table using the numerical representations to determine candidate pattern-types for the features wherein the look-up table was generated by using a number of sample patterns and at least one transformer that generates patterns by applying the transformer to the sample patterns; to select among the candidate pattern-types with selected validation modules that determine the pattern-type of each feature.

Other References

• Cavnar, W. et al., "Using Superimposed Coding of N-Gram Lists for Efficient Inexact Matching," Environmental Research Institute of Michigan, 13 pgs. (undated)
• Gage, P., "Random Access data Compression," C/C++ Users Journal, 15(9):23-28, 30 (Sep. 1997)
• Goldberg, D.E., Genetic Algorithm in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Company, Cover page, Title page, pp. 1-54, 94-97 (1989)
• Hecht-Nielsen, R., Neurocomputing, Addison-Wesley Publishing Company, Cover page, Title page, pp. 323-325, 413 and 415 (1990)
• Holland, J.H., Adaptation in Natural and Artificial Systems, A Bradford Book, The MIT Press, Cambridge, MA, Cover page, Title page, pp. 6-9 (1992)
• Ishii, K., "Design of a Recognition Dicctionary Using Distorted Characters," Systems and Computers in Japan, 21(9):35-44 (1990)
• Kinnear, Jr., K.E., "A Perspective on the Work in this Book", Advances in Genetic Programming, ed. Kenneth E. Kinnear, Jr., Cover page, pp. 4-19 (1993) (Title page information obtained from Dialog printout)
• Koza, J.R., "Introduction to Genetic Programming", Advances in Genetic Programming, ed. Kenneth E. Kinnear, Jr., Cover page, first pg unnumbered, pp. 22-25, 41-42 (1993) (Title page information obtained from Dialog printout)
• Kozek, T. et al., "Genetic Algorithm for CNN Template Learning," IEEE Transactions on Circuits and Systems I; Fundamental Theory and Applications, 40(6):392-402 (Jun. 1993)
• Li, Y. et al., "Validation of Image Defect Models for Optical Character Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(2):99-108 (Feb. 1996)
• Roussopoulos, N. et al., "Nearest Neighbor Queries," Proc. of the ACM SIGMOD International Conference on the Managemnet of Data (San Jose, CA), SIGMOD Record, 24(2):71-79 (Jun. 1995)
• White D. A. et al., "Similarity Indexing with the SS-tree, " Proc. 12th IEEE International Conference on Data Engineering (New Orleans, LA), pp. 516-523 (Feb. 1996