The handwriting of a person is an important biometric attribute of a human being which can be used to authenticate human identity. A number of biometric techniques have been proposed for personal identification in the past. Handwriting by an authorized person is considered to be the “seal of approval” and remains the most preferred means of authentication. Handwritten recognition has been an active and challenging problem. Most of the traditional methods have two challenges, due to the large variations of written text and the dependency relationship between letters. First, in real applications, words may be written cursively, so it is hard to identify the words automatically. Even if the words are neat, different people may write the same words in different styles. Since there are large shape variations in human handwriting, recognition accuracy of handwritten words is very difficult. The method presented in this paper consists of image prepossessing, geometric feature extraction, neural network training with extracted features and verification. A verification stage includes applying the extracted features of test handwriting to a neural network which will classify it as a genuine or forged. To recognize the handwritten words, the proposed work combines Artificial Neural Network (ANN) and Hidden Markov Model (HMM).

HMM, ANN

[1]. John Seiffertt. “Back propagation and Ordered in the Time Scales Calculus”, IEEE Transactions on Neural Networks, Vol. 21, No. 8,pp. 1262-1269. 2010.
[2]. Prashanth CR, KB Raja, KR Venugopal, LM Patnaik, ”Standard Scores Correlation based Offline signature verification system”, International Conference on advances in computing, control and telecommunication Technologies, 2009.
[3]. Ramachandra A.C, JyotiSrinivasRao “Robust Offline Signature Verification based on global features” IEEE International Advance Computing Conference, 2009.
[4]. M. Blumenstein. S. Armand. andMuthukkumarasamy, “Off-line Signature Verification using the Enhanced Modified Direction Feature and Neural based Classification”, International Joint Conference on Neural Networks, 2006.
[5]. Ozgunduz, E., Karsligil, E., and Senturk, T. “Off-line
Signature Verification and Recognition by Support
Vector Machine” Paper presented at the European
Signal processing Conference 2005.
[6]. B. Herbst. J. Coetzer. and J. Preez, “Online Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model”, EURASIP Journal on Applied Signal Processing.
[7]. S.Srihari. K. M. Kalera. and A. XU, “Offline Signature Verification and Identification Using Distance Statistics”, International Journal of Pattern Recognition And Artificial Intelligence ,vol. 18, no. 7, pp. 1339–1360, 2004.
[8]. Bunke, H., SamyBengio, and A. Vinciarelli. "Offline recognition of unconstrained handwritten texts using HMMs and statistical language models", Pattern Analysis and Machine Intelligence, IEEE Transactions on 26.6 (2004): 709-720.
[9]. Martinez, L.E, Travieso, C.M, Alonso, J.B., and Ferrer, M. "Parameterization of a forgery Handwritten Signature Verification using SVM", IEEE 38thAnnual 2004 International Carnahan Conference on Security Technology, 2004 PP.193-196.
[10]. Bunke, H., SamyBengio, and A. Vinciarelli. "Offline recognition of unconstrained handwritten texts using HMMs and statistical language models."Pattern Analysis and Machine Intelligence, IEEE Transactions on 26.6 (2004): 709-720.
[11]. Velez, J.F., Sanchez, A. and Moreno A.B. "Robust Off-Line Signature Verification using Compression", Networks and Position Cuttings 2003.
[12]. Jain, A., Griess, F., and Connel1, S. “Online Signature Recognition”, Pattern Recognition, vol.35,2002, pp 29632972.
[13]. X. Feng, A.K. I. Williams, and S. Felderhof, “Combining Belief Networks and Neural Networks for Scene Segmentation”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 467-483, Apr. 2002.
[14]. N. Arica and F. Yarman-Vural, “An Overview of Character Recognition Focused on Off line Handwriting”, IEEE Transactions on Systems, Man, and Cybernetics, Part C:Applications and Reviews, Vol.31 (2), pp. 216 – 233. 2001.
[15]. Madhvanath, Sriganesh, and VenuGovindaraju. "The role of holistic paradigms in handwritten word recognition" Pattern Analysis and Machine Intelligence, IEEE Transactions on 23.2 (2001): 149-164.
[16]. Pacut, A. and Czaja, A. 2001. "Recognition of Human Signatures. Neural Network", in proceedings of the International Conference on Neural Network, IJCNN‟01, vol.2,pp 1560-1564.
[17]. J Edson, R. Justino, F. Bortolozzi and R. Sabourin, "An off-line signature verification Using HMM for Random,Simple and Skilled Forgeries", Sixth International Conference on Document Analysis and Recognition, pp.1031-1034, Sept.2001. 211-222, Dec.2000
[18]. R. Plamondon and S.N. Srihari, "Online and Offline Handwriting Recognition: A Comprehensive Survey", IEEE Tran. on Pattern Analysis and Machine Intelligence, vol.22 no.1, pp.63-84, Jan.2000.
[19]. Sonsone and Vento. “Signature Verification: Increasing Performance by Multi-Stage System”, Pattern Analysis & Application, vol.3, no. 2, 2000, pp.169-181.
[20]. F. Cesarini, M. Gori, S. Marinai, and G. Soda, “INFORMys: "A Flexible Invoice-Like Form Reader System", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 7, pp. 730-745, July 1998.
[21]. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-Based Learning Applied to Document Recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278-2324,1998.
[22]. R.G. Casey and E. Lecolinet, “A Survey of Methods and Strategies in Character Segmentation”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 7, pp. 690-706, July 1996.
[23]. L. O Gorman and R. Kasturi, Document Image Analysis. Los Alamitos, Calif.: IEEE Press, 1995.John Canny. "A computational approach to edge detection. Pattern Analysis and Machine Intelligence", IEEE Transactions on, PAMI-8(6):679–698, Nov. 198.