Eastman-Kodak Company

Robert Y Chen

James A Mason

John F Cook

Gregory J Martin

A character recognition method comprising the following steps: (1) acquiring a two dimensional array of pixels, (2) locating an unknown character in the two dimensional array, (3) computing weighted correlation coefficients between the unknown character and a trained set of characters (i.e. a font), (4) recognizing the unknown character as the trained character with the highest weighted correlation coefficient above a threshold. The weights in the correlation calculations are adjusted to place more emphasis on those areas of a trained character that distinguishes it from all other trained characters in the training set. A method for optimally adjusting these weights is described herein.

Character recognition method using optimally weighted correlation

A character recognition method comprising the following steps: (1) acquiring a two dimensional array of pixels, (2) locating an unknown character in the two dimensional array, (3) computing statistically weighted correlation coefficients between the unknown character and a trained set of characters (i.e. a font), (4) recognizing the unknown character as the trained character with the highest correlation coefficient above a threshold. The weights in the correlation calculations are adjusted to place more emphasis on those areas of a character that exhibit less noise and less emphasis on those areas of the character that exhibit more noise. A method for determining and using these weights is described herein.

Character recognition method using statistically weighted correlation

A character recognition method comprising the following steps: (1) acquiring a two dimensional array of pixels, (2) locating an unknown character in the two dimensional array, (3) computing correlation coefficients between an unknown character and a trained set of characters (i.e., a font), (4) recognizing the unknown character as the trained character with the highest correlation coefficient above a threshold. The manner in which trained characters are chosen is described herein as a correlation search. Correlation search results in a fast means of correctly classifying an unknown character from a large training set, often on the second or third try, depending on the inner-correlations of the trained characters.

Growing community of inventors

Seattle, WA, United States of America

Robert Y Chen