1. A method of recognition of characters from a bit-mapped graphic file received from an optical scanning device comprising a template cache for recognized characters, feature analysis means, context analysis means, said template cache along with said context analysis means are used as a first shape characteristic analyzing means, said feature analysis means along with said context analysis means are used as a second shape characteristic analyzing means and as a training means for template cache, said method comprising the steps of initially emptying the template cache; analyzing the shape characteristics of each character by said first shape characteristic analyzing means; analyzing the shape characteristics of each character by said second shape characteristic analyzing means if no or not enough reliability of recognition is achieved after the template cache analyzing; analyzing the results of said shape analyzing routines by said context analysis means; sending results of said context analysis to said template cache for template training; and training said template cache comprising: comparing a resemblance of a bit-mapped image recognized by one of said first and second shape characteristic analyzing means with existing templates using a weighted total of pixel intensity differences of compared images as a measure of similarity thereof; generating a new template if no resemblance is found; adjusting the existing template if resemblance is found comprising the steps of correcting an averaged template image corresponding to said existing template by said new bit-mapped image; and increasing a reliability value of said existing template by the new bit-mapped image reliability value; marking the said existing template as open for training if its reliability value is lower than the predefined reliability level; marking the said existing template as closed for training if its reliability value is higher or equal to the predefined reliability level.
5. The method as recited in claim 1 wherein the each template comprises a template image determined as an averaged total of images received for training.