Online Signature Verification System for Kaisyo Script Based on Structured Learning and Segmentation of HMM

Abstract

This paper presents a new Hidden Markov Model (HMM) for the online signature verification of oriental characters such as Japanese and Chinese. These oriental characters usually consist of many individual strokes such as dots and straight lines. Taking into account of this characteristic, a new HMM is proposed, which is composed of many sub-models each of which corresponds to an individual stroke. In addition, the 'pen-up' state which represents the movement between strokes is explicitly introduced. Then, the parameter re-estimation scheme for this special class of HMM is derived exploiting the structure of the proposed HMM. Thanks to the structured learning mechanism, the proposed HMM not only can drastically reduce the computational time necessary for the learning process but also shows higher recognition performance for the rejection of the skilled forgery. Finally, the usefulness of the proposed scheme is demonstrated by comparing it with the conventional models.