Hand signature and handwriting recognition as identification of the writer using gray level cooccurrence matrix and bootstrap

The pattern of signature and handwriting are unique, so they can be utilised as an authentication system. This research proposed a method of signature and handwriting recognition on a mobile device using the Gray Level Co-occurrence Matrix (GLCM) for texture-based feature extraction and the bootstrap for performing single classifier model. The proposed method is successfully implemented in the offline and online application. The offline experiment of signature and handwriting from the same user produces accuracy 100%. In a cross evaluation using different users as model and target, the experiment performs accuracy around 34% and 44% for signature and handwriting data, respectively. In the case study of the training and testing data from the same user on mobile devices, the experiment using stylus and finger produces accuracy 84.62% and 88.46%, respectively for online signature recognition, and 70% and 90% for online handwriting recognition.