Text-Independent Writer Identification Based on Fusion of Dynamic and Static Features

摘要

Handwriting recognition is a traditional and natural approach for personal authentication. Compared to signature verification, text-independent writer identification has gained more attention for its advantage of denying im-posters in recent years. Dynamic features and static features of the handwriting are usually adopted for writer identification separately. For text-independent writer identification, by using a single classifier with the dynamic or the static feature, the accuracy is low, and many characters are required (more than 150 characters on average). In this paper, we developed a writer identification method to combine the matching results of two classifiers which employs the static feature (texture) and dynamic features individually. Sum-Rule, Common Weighted Sum-Rule and User-specific Sum-Rule are applied as the fusion strategy. Especially, we gave an improvement for the user-specific Sum-Rule algorithm by using an error-score. Experiments were conducted on the NLPR handwriting database involving 55 persons. The results show that the combination methods can improve the identification accuracy and reduce the number of characters required.