Abstract
The signature is defined as special kind of handwriting that includes special characters and flourishes. Handwritten signatures are most accepted individual attribute for individuality authentication of the person. This provides novel method for the signature recognition and verification by using zone based statistical features. It contains mainly two phases. During the first phase, the knowledge base is constructed by training samples using the zone wise statistical features. During second stage i.e., testing phase, the processed image is obtained having zoning wise statistical features and signature is recognized using neural network classifiers. An accuracy rate of 97.5% is achieved by testing 200 samples. MATLAB is used for designing this signature recognition and verification system and is robust and avoids noise, blur and change in size, lightening conditions and other possible degradation.
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References
Anand, H.: “Realtive study on signature verification & recognition system. Int. J. Innov. Res. Adv. Eng. (IJIRAE) 1(5) (2014). ISSN 2349 - 2163
Abikoye, O.C.: Offline signature recognition & verification using neural networks. Int. J. Comput. Appl. (0975 – 8887) 35(2), 44–51 (2011)
Tomar, M.: A directional feature with energy base offline signature verification networks. Int. J. Soft Comput. (IJSC) 2(1) (2011)
Choudhary, N.Y.: Signature recognition & verification system using back propagation neural networks. Int. J. IT Eng. Appl. Sci. Res. (IJIEASR) 2(1), 1–8 (2013). ISSN 2319-4413
Odeh, S.: Apply multi large perceptron neural network for off-line signature verification & recognition. IJCSI Int. J. Comput. Sci. Issues 8(6), 261 (2011). No 2, ISSN 1694-0814
Kalenova, D.: Personal authentication using signature recognition. IEEE 85, 1437–1462 (2004)
Cemil, O.Z., Ercal, F., Demir, Z.: Offline signature recognition & verification with artificial neural networks. IJETAE 3(8), 19 (2014)
Adebaya Daramola, S.: Offline signature recognition using Hidden Markov model. Int. J. Comput. Appl. (0975 – 8887) 10(2), 17–22 (2010)
Sthapak, S.: Artificial neural networks based signature recognition & verification. Int. J. Emerg. Technol. Adv. Eng. 3(8), 191–197 (2013)
Pushalatha, K.N.: Offline signature verification with random & skilled forgery detection using polar domain feature & multi stage classification regression model. Int. J. Adv. Sci. Technol. (IJAST) 59, 27–40 (2013)
Kumar, P.: Hand written signature recognition & verification using neural networks. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(3) (2013). ISSN 2277 128X
Bharadi, V.A.: Offline signature recognition system. Int. J. Comput. Appl. (IJCA) (0975 - 8887) 1(27) (2010)
Kumar, M.: Signature verification using neural network. Int. J. Comput. Sci. Eng. (IJCSE) 4(09), 1498 (2012)
Gill, R., Singh, M.: Statistical features based off line signature verification system using image processing techniques. Int. J. Sci. Res. (IJSR) 1(3) (2012)
Angadi, S.A., Kodabagi, M.M., Jerabandi, M.V.: Character recognition of Kannada text in low resolution display board images using zone wise statistical features. In: IEEE World Congress on Information and Communication Technologies, pp. 61–66 (2012)
Kodabagi, M.M., Kembavi, S.B.: recognition of basic Kannada characters in scene images using euclidean distance classifier. Int. J. Comput. Eng. Technol. (IJCET) 4, 632–641 (2013)
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Lakkannavar, B.F., Kodabagi, M.M., Naik, S.P. (2020). Signature Recognition and Verification Using Zonewise Statistical Features. In: Pandian, A., Palanisamy, R., Ntalianis, K. (eds) Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019). ICCBI 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-030-43192-1_83
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DOI: https://doi.org/10.1007/978-3-030-43192-1_83
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