Study of accuracy dependence of writer recognition on number of characters

A new off-line writer recognition method is proposed which uses a 2D membership function obtained by piling up characters with /spl alpha/-cuts. Furthermore, principal component analysis is used to characterize the handwriting features and a dictionary of selected characters (those that are not far from the mean character) is constructed. The writer recognition is done by the production of similarities between three types of characters. This method was applied to writer recognition of 108 students who wrote 46 types of "hiragana" characters and a 97.8% recognition ratio was obtained.