Abstract: Character images which are to be sent to a neural network trained to recognize a predetermined set of symbols are first processed by an optical character recognition pre-processor which normalizes the character images. The output of the neural network is processed by an optical character recognition post-processor. The post-processor corrects erroneous symbol identifications made by the neural network. The post-processor identifies special symbols and symbol cases not identifiable by the neural network following character normalization. For characters identified by the neural network with low scores, the post-processor attempts to find and separate adjacent characters which are kerned and characters which are touching. The touching characters are separated in one of nine successively initiated processes depending upon the geometric parameters of the image.

Abstract: Characters which touch one another in a document image are identified and separated in accordance with their contours. The objects in the image are divided into two classes comprising, respectively, those having closed inner contours and those which do not. An object in the first class having either a closed inner contour which is not centered in the object or two horizontally offset closed inner contours or more than two closed inner contours is identified as containing two touching characters and is split in accordance with the character stroke thickness observed in the document. An object in the second class whose contours intersect three spaced horizontal reference lines at more than a predetermined number of points is compared against intersection patterns characteristic of single characters. If a match is not found, the object is identified as containing touching characters.