Abstract
In this paper, an approach for off-line Uyghur signature recognition is proposed. The signature images were preprocessed using improved techniques adapted to the Uyghur signature. The preprocessing are included noise reduction, binarization, normalization and thinning. Two types of preprocessing steps were conducted with and without thinning. The directional features, global baseline, upper and lower line features, local central features were extracted respectively after the two kinds of preprocessing. Experiments were performed selecting Euclidean distance and Chi-square distance based measure methods and using K nearest neighbor classifier for Uyghur signature samples from 50 different people with 1000 signatures. A correct recognition rate of 96.0% was achieved with thinning. The experimental results indicated that thinning has significant importance to the extracted features and its effects to the accuracy were related with the nature of extracted features.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Plamondon, R.: Progress in Automatic Signature Verification. World Scientific Publ., Singapore (1994)
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)
Kisku, D.R., Gupta, P., Sing, J.K.: Offline signature identification by fusion of multiple classifiers using statistical learning theory. International Journal of Security and Its Applications 4(3), 35–45 (2010)
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)
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)
Karounia, A., Dayab, B., Bahlak, S.: Offline signature recognition using neural networks approach. Procedia Computer Science 3, 155–161 (2011)
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)
Ubul, K., Adler, A., Abliz, G., Yasheng, M., Hamdulla, A.: Off-line Uyghur signature recognition based on modified grid information features. In: 11th International Conference on Information Sciences, Signal Processing and their Applications, pp. 1083–1088. IEEE press, New York (2012)
Hilditch, C.J.: Linear skeletons from square cupboards. Machine Intelligence 4, 403–420 (1969)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ubul, K., Adler, A., Yadikar, N. (2012). Effects on Accuracy of Uyghur Handwritten Signature Recognition. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_67
Download citation
DOI: https://doi.org/10.1007/978-3-642-33506-8_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33505-1
Online ISBN: 978-3-642-33506-8
eBook Packages: Computer ScienceComputer Science (R0)