Automatic recognition of handwritten or printed text is a method that has been developing since the inception of computers, and various improvements in speed and quality of detection are still being discovered to this day. It can be made for general use, or specialized for a certain script or amount of information given. With specialized techniques, it is important to consider the type of script that is recognized, and other data we can infer from the given characters. For writing systems that have a large set of potential characters, it is necessary to reduce the number of graphic comparisons in order to optimize speed. This can be done with predictive methods in the case of detection of a larger amount of text, and in the case of a smaller set, decomposition methods can be used. In this paper, the detection of handwritten text of the Japanese kanji alphabet is implemented. The optimization is performed by analyzing and comparing the number of strokes of the drawn character, and by dividing the character into a radical and a sub-character which can simultaneously determine the partial probabilities of several characters containing an identical radical. The results determine if further recursive calculation of probability is needed. An analysis of the program quality was also made depending on the adjustable program settings, using a test case consisting of a printed set of all characters that the program can potentially recognize.