Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Plamondon, R., Srihari, S.N.: On-line and off-line handwriting recognition: a comprehensive survey. IEEE Transaction on Pattern Analysis and Machine Intelligence 22(1), 63–84 (2000)
Ismail, M.A., Gad, S.: Off-line Arabic signature recognition and verification. Pattern Recognition 33(10), 1727–1740 (2000)
Plamondon, R., Lorette, G.: Automatic signature verification and writer identification – the state of the art. Pattern Recognition 22(2), 107–131 (1989)
Guru, D., Prakash, H.: Online Signature Verification and Recognition: An Approach Based on Symbolic Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(6), 1059–1073 (2009)
Huang, K., Yan, H.: Off-line Signature Verification Based on Geometric Feature Extraction and Neural Network Classification. Pattern Recognition 30(1), 9–17 (1997)
Lv, H., Wang, W., Wang, C., Zhuo, Q.: Off-line Chinese signature verification based on support vector machines. Pattern Recognition Letters 26(15), 2390–2399 (2005)
Kisku, D.R., Gupta, P., Sing, J.K.: Off-line signature identification by fusion of multiple classifiers using statistical learning theory. International Journal of Security and Its Applications 4(3), 35–45 (2010)
Yingyong, Q., Hunt, B.: Signature Verification Using Global and Grid Features. Pattern Recognition 22(12), 1621–1629 (1994)
Baltzakisa, H., Papamarkos, N.: A new signature verification technique based on a two-stage neural network classifier. Engineering Applications of Artificial Intelligence 14(1), 95–103 (2001)
Shanker, A., Rajagopalan, A.: Off-line signature verification using DTW. Pattern Recognition Letters 28(12), 1407–1414 (2007)
Ghandali, S., Moghaddam, M.E.: Off-line Persian signature identification and verification based on image registration and fusion. Journal of Multimedia 4(3), 137–144 (2009)
Vargas, J., Ferrer, M., Travieso, C., Alonso, J.: Off-line signature verification based on grey level information using texture features. Pattern Recognition 44(2), 375–385 (2011)
Ubul, K., Adler, A., Abliz, G., Yasin, M., Hamdulla, A.: Off-Line Uyghur Signature Recognition Based on Modified Grid Information Features. In: The 11th International Conference on Information Sciences, Montreal, Canada, July 3-5 (2012)
Ubul, K., Adler, A., Yadikar, N.: Effects on Accuracy of Uyghur Handwritten Signature Recognition. In: Liu, C.-L., Zhang, C., Wang, L. (eds.) CCPR 2012. CCIS, vol. 321, pp. 548–555. Springer, Heidelberg (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ubul, K., Yibulayin, T., Aysa, A. (2014). Off-Line Uyghur Handwritten Signature Verification Based on Combined Features. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_52
Download citation
DOI: https://doi.org/10.1007/978-3-662-45643-9_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45642-2
Online ISBN: 978-3-662-45643-9
eBook Packages: Computer ScienceComputer Science (R0)