Character recognizing method

TLDR: In this article, the covariance value matrix of feature values from a mean feature value and variance values obtained from extracted feature values as a dictionary and using it for decision making at a high speed.

Abstract: PURPOSE:To obtain a character recognizing device which has a high rate of character recognition by defining the covariance value matrix of feature values from a mean feature value and variance values obtained from extracted feature values as a dictionary and using it for decision making at a high speed. CONSTITUTION:A feature extraction part 1 extracts feature values from respective character data and stores them in a feature value storage part 2, and a feature value processing part 3 calculates the mean feature value and variance values from the feature values. A feature selection part 4 selects a feature effective to the representation of character data on the basis of the mean feature value and variance values. A covariance processing part 5 fetches the feature value corresponding to the feature selected by the feature selecting part 4 on the basis of some feature value in the feature value storage part 2 and the covariance matrix value is stored in a dictionary storage part 6 as a dictionary. The feature value from the feature extraction part 1, on the other hand, is supplied to a similarity calculation part 7, which calculates a similarity value from the signal from a dictionary storage part 6; and a candidate character storage part 8 determines and stores a candidate character category. A tournament processing part 9 generates a linear identification function from the covariance value matrix corresponding to the candidate character category name and obtains the decision result with the function.