Abstract
The handwritten signature is the most common method used to verify the legality of documents and plays a critical role in indicating a person’s identity. Traditionally, a person determines the validity of a signature by manually comparing it to a stored record of genuine signatures. Manual verification is time-consuming and depends on the skill of the verifier to detect forgeries. This work aims to develop a system for handwritten signature recognition using pattern recognition techniques. This work pretends to contribute to banks and companies to validate a document’s signature automatically. Handwritten signature verification systems have been approached using different methods; however, solutions with artificial intelligence stand out for their superior performance. The proposed model is based on a shallow convolutional neural network and trained with the CEDAR handwritten signature dataset. The model can recognize the handwritten signatures of 55 users, verifying their legality against forgeries with an Equal Error Rate of 1.716%, improving the performance described by other methods working on the same dataset. The developed system is lightweight and allows verification to be performed in real time. Additionally, this paper provides insightful analysis for hyperparameter optimization of the model.
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References
Stewart, L.F.: The process of forensic handwriting examinations. Forensic Res. Criminol. Int. J. 4, 00126 (2017)
Hafemann, L.G., Sabourin, R., Oliveira, L.S.: Offline handwritten signature verification—literature review. In: 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1–8. IEEE (2017)
American Bankers Association: ABA Deposit Account Fraud Survey Report. American Bankers Association (2020)
Jain, A., Singh, S.K., Singh, K.P.: Handwritten signature verification using shallow convolutional neural network. Multimedia Tools Appl. 79(27–28), 19993–20018 (2020). https://doi.org/10.1007/s11042-020-08728-6
Prakash, G.S., Sharma, S.: Offline signature verification and forgery detection based on computer vision and fuzzy logic. IAES Int. J. Artif. Intell. (IJ-AI) 3, 156–165 (2014)
Barbantan, I., Vidrighin, C., Borca, R.: An offline system for handwritten signature recognition. In: 2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing, pp. 3–10. IEEE (2009)
Mohammed, R.A., Nabi, R.M., Sardasht, M., Mahmood, R., Nabi, R.M.: State-of-the-art in handwritten signature verification system. In: 2015 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 519–525. IEEE (2015)
Jindal, U., Dalal, S., Dahiya, N.: A combine approach of preprocessing in integrated signature verification (ISV). Int. J. Eng. Technol. 7, 155–159 (2018)
Alvarez, G., Sheffer, B., Bryant, M.: Offline signature verification with convolutional neural networks. Technical report (2016)
Kumar, A., Bhatia, K.: A survey on offline handwritten signature verification system using writer dependent and independent approaches. In: 2016 2nd International Conference on Advances in Computing, Communication, and Automation (ICACCA) (Fall), pp. 1–6. IEEE (2016)
Souza, V.L.F., Oliveira, A.L.I., Sabourin, R.: A writer-independent approach for offline signature verification using deep convolutional neural networks features. In: 2018 7th Brazilian Conference on Intelligent Systems (BRACIS), pp. 212–217. IEEE (2018)
Jagtap, A.B., Hegadi, R.S., Santosh, K.C.: Feature learning for offline handwritten signature verification using convolutional neural network. Int. J. Technol. Hum. Interact. (IJTHI) 15, 54–62 (2019)
Hatkar, P.V, Salokhe, B.T., Malgave, A.A.: Offline handwritten signature verification using neural network. Methodology 2, 1–5 (2015)
Alajrami, E., et al.: Handwritten signature verification using deep learning. Int. J. Acad. Multidiscipl. Res. (IJAMR) 3 (2020)
Hafemann, L.G., Sabourin, R., Oliveira, L.S.: Learning features for offline handwritten signature verification using deep convolutional neural networks. Pattern Recogn. 70, 163–176 (2017)
Souza, V.L.F., Oliveira, A.L.I., Cruz, R.M.O., Sabourin, R.: A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification. Expert Syst Appl. 154, 113397 (2020)
Wirth, R., Hipp, J.: CRISP-DM: towards a standard process model for data mining. In: Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, pp. 29–39. Manchester (2000)
Yeung, D.-Y., Chang, H., Xiong, Y., George, S., Kashi, R., Matsumoto, T., Rigoll, G.: SVC2004: first international signature verification competition. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 16–22. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-25948-0_3
Vargas, F., Ferrer, M., Travieso, C., Alonso, J.: Off-line handwritten signature GPDS-960 corpus. In: Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), pp. 764–768. IEEE (2007)
Kalera, M.K., Srihari, S., Xu, A.: Offline signature verification and identification using distance statistics. Int. J. Pattern Recogn. Artif. Intell. 18, 1339–1360 (2004)
Ortega-Garcia, J., et al.: MCYT baseline corpus: a bimodal biometric database. IEE Proc. Vision Image Sig. Process. 150, 395–401 (2003)
Cinthia, O.d.A., et al.: Bases de Dados de Cheques BancáriosBrasileiros (2000)
Yang, L., Shami, A.: On hyperparameter optimization of machine learning algorithms: theory and practice. Neurocomputing 415, 295–316 (2020). https://doi.org/10.1016/J.NEUCOM.2020.07.061
Feurer, M., Hutter, F.: Hyperparameter optimization. In: Hutter, F., Kotthoff, L., Vanschoren, J. (eds.) Automated Machine Learning. TSSCML, pp. 3–33. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05318-5_1
Biewald, L.: Experiment Tracking with Weights and Biases (2022). https://www.wandb.com/
Chen, S., Srihari, S.: A new off-line signature verification method based on graph. In: 18th International Conference on Pattern Recognition (ICPR 2006), pp. 869–872 (2006). https://doi.org/10.1109/ICPR.2006.125
Kumar, R., Sharma, J.D., Chanda, B.: Writer-independent off-line signature verification using surroundedness feature. Pattern Recogn. Lett. 33, 301–308 (2012). https://doi.org/10.1016/j.patrec.2011.10.009
Bharathi, R.K., Shekar, B.H.: Off-line signature verification based on chain code histogram and support vector machine. In: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 2063–2068 (2013). https://doi.org/10.1109/ICACCI.2013.6637499
Guerbai, Y., Chibani, Y., Hadjadji, B.: The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters. Pattern Recogn. 48, 103–113 (2015)
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Maza-Merchán, C., Cordero, J. (2023). Handwritten Signature Verification System Using Convolutional Neural Network for Real-Time Applications. In: Botto-Tobar, M., Zambrano Vizuete, M., Montes León, S., Torres-Carrión, P., Durakovic, B. (eds) Applied Technologies. ICAT 2022. Communications in Computer and Information Science, vol 1755. Springer, Cham. https://doi.org/10.1007/978-3-031-24985-3_8
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