The accuracy of an Optical Character Recognition (OCR) process' ability to recognize characters continues to improve. However, even the best OCR processes are incapable of providing one hundred per-cent accuracy. Accuracy is dependent on many factors including what fonts are being scanned and the ability to recognize the scanned fonts. One process, utilized for improving the accuracy of hand written or unfamiliar fonts, uses a training method consisting of one sample or an average of a multiplicity of character samples to create a character master for comparing scanned characters images. A probability of closeness and/or the process of elimination of other character candidates is used to select a best fit. One disadvantage of such a process is the need for retraining for a user other than the trained user. A second disadvantage revolves around the need to maintain permanent additional storage to maintain the training material.
Other processes have employed probabilistic color distribution in characters to improve the accuracy of character recognition. The use of probabilistic color distribution recognizes that certain colors are undetectable to the human eye. One problem arises when a color is misinterpreted or the OCR process is not sure of a color associated with specific characters. Other problems can result from color output variations due to a printing process such as ink changes, weather changes (temperature, humidity), print head positioning adjustment failures, etc.
Consequently, a technique is needed that provides a procedure for improving the accuracy of an OCR process by providing for the recognition of characters that would otherwise not be recognized.