Online writer identification using character prototypes distributions

Writer identification is a process which aims to identify the writer of a given handwritten document. Its implementation is needed in applications such as forensic document analysis and document retrieval which involved the use of offline handwritten documents. With the recent advances of technology, the invention of digital pen and paper has extended the field of writer identification to cover online handwritten documents. In this communication, a methodology is proposed to solve the problem of text-independent writer identification using online handwritten documents. The proposed methodology would strive to identify the writer of a given handwritten document regardless of its text contents by comparing his or her handwritings with those stored in a reference database. The output of this process would be a ranked list of the writers whose handwritings are stored in the reference database. The main idea is to use the distance measurement between the distributions of reference patterns defined at the character level. Very few, if any, attempts have been done at this character level. Two sets of handwritten document databases each with 82 online documents contributed by 82 subjects were used in the experiments. The reported result was 95% of Top 1 rate accuracy. Only four writers were identified wrongly, ranked as 2, 4, 5 and 12 choice returned.