Off-Line Uyghur Handwritten Signature Verification Based on Combined Features

An off-line Uyghur handwritten signature verification method based on combined features was proposed in this paper. Firstly, the signature images were preprocessed using techniques adapted to the Uyghur signature. The preprocessing included noise reduction, binarization, and normalization. Then, the global features, local features which each of them include several features were extracted respectively after the preprocessing, and they are combined together. Finally, two types of classifiers, Euclidean distance classifier, and non-linear SVM classifier are used to classify 75 genuine signatures and 36 random forgeries in our experiment. Two kinds of experiments were performed for and variations in the number of training and testing datasets. Experiments indicate that the combination of directional features with local central point features has obtained 2.26% of FRR and 2.97% of FAR with SVM classifier. The experimental results indicated that the combination method can capture the nature of Uyghur signature and its writing style effectively.