Off-line handwritten signature recognition by wavelet entropy and neural network

Handwritten signatures are widely utilized as a form of personal recognition. However, they have the unfortunate shortcoming of being easily abused by those who would fake the identification or intent of an individual which might be very harmful. Therefore, the need for an automatic signature recognition system is crucial. In this paper, a signature recognition approach based on a probabilistic neural network (PNN) and wavelet transform average framing entropy (AFE) is proposed. The system was tested with a wavelet packet (WP) entropy denoted as a WP entropy neural network system (WPENN) and with a discrete wavelet transform (DWT) entropy denoted as a DWT entropy neural network system (DWENN). Our investigation was conducted over several wavelet families and different entropy types. Identification tasks, as well as verification tasks, were investigated for a comprehensive signature system study. Several other methods used in the literature were considered for comparison. Two databases were used for algorithm testing. The best recognition rate result was achieved by WPENN whereby the threshold entropy reached 92%

Offline Signature Verification System Based On The Online Data

Most of the signature verification work done in the past years focused either on offline or online approaches. In this paper, a different methodology is proposed, where the online reference data acquired through a digitizing tablet serves as the basis for the segmentation process of the corresponding scanned offline data. Local windows are built over the image through a self-adjustable learning process and are used to focus on the feature extraction step. The windows positions are determined according to the complexity of the underlying strokes based on the observation of a delta-lognormal handwritten reproduction model. Local features extraction that takes place focused on the windows formed, and it is used in conjunction with the global primitives to feed the classifier. The overall performance of the system is then measured with three different classification schemes.2008Plamondon, R., Lorette, G., Automatic signature verification and writer identificationthe state of the art (1989) Pattern Recognition, 22 (2), pp. 107-131Leclerc, F., Plamondon, R., Automatic signature verification: The state of the Art19891993 (1994) International Journal of Pattern Recognition and Artificial Intelligence, 8 (3), pp. 643-660Plamondon, R., Srihari, S.N., On-line and off-line handwriting recognition: A comprehensive survey (2000) IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (1), pp. 63-84Quan, Z., Huang, D., Xia, X., Lyu, M.R., Lok, T.-M., Spectrum analysis based on windows with variable widths for online signature verification Proceedings of the 18th International Conference on Pattern Recognition (ICPR 06), 2, pp. 1122-1125. , August 2006 Hong KongKhan, M.K., Khan, M.A., Khan, M.A.U., Ahmad, I., On-line signature verification by exploiting inter-feature dependencies Proceedings of the 18th International Conference on Pattern Recognition (ICPR 06), 2, pp. 796-799. , August 2006 Hong KongLei, H., Palla, S., Govindaraju, V., ER 2: An intuitive similarity measure for on-line signature verification Proceedings of the 9th International Workshop on Frontiers in Handwriting Recognition (IWFHR 04), pp. 191-195. , October 2004 Tokyo, JapanArmand, S., Blumenstein, M., Muthukkumarasamy, V., Off-line signature verification based on the modified direction feature Proceedings of the 18th International Conference on Pattern Recognition (ICPR 06), 4, pp. 509-512. , August 2006 Hong KongZhang, B., Off-line signature recognition and verification by Kernel principal component self-regression Proceedings of the 5th International Conference on Machine Learning and Applications (ICMLA06), pp. 28-33. , December 2006 Orlando, Fla, USAFerrer, M.A., Alonso, J.B., Travieso, C.M., Offline geometric parameters for automatic signature verification using fixed-point arithmetic (2005) IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (6), pp. 993-997Brault, J.-J., Plamondon, R., Segmenting handwritten signatures at their perceptually important points (1993) IEEE Transactions on Pattern Analysis and Machine Intelligence, 15 (9), pp. 953-957Zimmer, A., Ling, L.L., Preprocessing: Segmenting by stroke complexity Proceedings of the 6th Iber-American Symposium on Pattern Recognition, pp. 89-94. , 2001 Florianpolis, SC, BrazilDrouhard, J.P., Sabourin, R., Godbout, M., A neural network approach to off-line signature verification using directional PDF (1996) Pattern Recognition, 29 (3), pp. 415-424Guerfali, W., Plamondon, R., The delta lognormal theory for the generation and modeling of cursive characters Proceedings of the 3rd International Conference on Document Analysis and Recognition (ICDAR 95), 1, pp. 495-498. , August 1995 Montreal, CanadaZimmer, A., Ling, L.L., A model-based signature verification system Proceedings of the 1st IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS 07), pp. 1-6. , September 2007 Crystal City, Va, USAPlamondon, R., Guerfali, W., Why handwriting segmentation can be misleading? Proceedings of the 13th International Conference on Pattern Recognition (ICPR 96), 4, pp. 396-400. , August 1996 Vienna, AustriaPlamondon, R., Guerfali, W., The generation of handwriting with delta-lognormal synergies (1998) Biological Cybernetics, 78 (2), pp. 119-132Gerald, C.F., Wheatley, P.O., (1989) Applied Numerical Analysis, , New York, NY, USA Addison-WesleyOgorman, L., Curvilinear feature detection from curvature estimation Proceedings of the 9th International Conference on Pattern Recognition, 2, pp. 1116-1119. , 1988 Rome, ItalyOtsu, N., A threshold selection method from gray level histograms (1979) IEEE Transactions on Systems Man and Cybernetics, 9 (1), pp. 62-66Gonzalez, R., Woods, R., (1992) Digital Image Processing, , New York, NY, USA Addison-WesleyMardia, K.V., (1972) Statistics of Directional Data, , San Diego, Calif, USA Academic Pres

Offline signature verification system based on the online data

Most of the signature verification work done in the past years focused either on offline or online approaches. In this paper, a different methodology is proposed, where the online reference data acquired through a digitizing tablet serves as the basis for the segmentation process of the corresponding scanned offline data. Local windows are built over the image through a self-adjustable learning process and are used to focus on the feature extraction step. The window's positions are determined according to the complexity of the underlying strokes based on the observation of a delta-lognormal handwritten reproduction model. Local features extraction that takes place focused on the windows formed, and it is used in conjunction with the global primitives to feed the classifier. The overall performance of the system is then measured with three different classification schemes. Copyright (c) 2008 A. Zimmer and L. L. Ling