Character recognition method using optimally weighted correlation

A method of character recognition, comprising the steps of:

• 1) creating a font of trained characters by:
• (a) acquiring an image composed of a two dimensional array of pixels;
• (b) locating all of the characters in the image by selectively scanning columns or rows of a predetermined area of the image and comparing each pixel's intensity with a reference level to determine the first pixel of each character and recording the location (column and row coordinates) of such pixel and identifying the other pixels adjacent to the first whose intensity also exceeds the reference level and recording the upper left and lower right coordinates of a box bounding each character;
• (c) identifying (labeling) all located characters and entering such identified characters as trained characters in memory;
• (d) creating a set of weights, initialized to a constant value, for all trained characters of the training set;
• (e) computing a correlation matrix composed of weighted correlation coefficients for all possible pairs of trained characters comprising the trained character set;
• (f) searching through the correlation matrix and identifying the character corresponding to the row of the correlation matrix containing the most highly correlated pair of trained characters;
• (g) adjusting the weights of the trained character identified in (f);
• (h) recomputing the row of the correlation matrix corresponding to the trained character identified in (f) using the adjusted weights computed in (g);
• and
• (i) repeating steps (f) through (h) until the highest correlation in the correlation matrix is reduced to an acceptable level or until a maximum count is exceeded and eliminating those trained characters from this iterative process that have been selected an excessive number of times;
• and
• 2) recognizing unknown characters by:
• (j) acquiring a two dimensional array of pixels;
• (k) locating all unknown characters in a manner described in (b);
• (l) computing weighted correlation coefficients using the weights determined in steps (a) through (i) between all unknown characters and the trained character set;
• and
• (m) identifying all unknown characters as those trained characters with the highest weighted correlation coefficients above a threshold.