Abstract
Despite recent widespread research in the field, handwritten signature verification is still an unresolved research problem. A person’s signature is an important biometric trait of a human that can be used to verify a person’s identification. There are two primary biometric identification methods: (i) A method of identification based on vision and (ii) An identification method without the use of vision. Examples of vision-based identification include face reading, fingerprint identification, and retina scanning. The other examples for non-vision-based identification include speech recognition and signature verification. In financial, commercial, and legal activities, signatures are crucial. Two methods are widely studied and investigated for signature verification: the online method (dynamic method) and the offline method (Static approach). Offline systems are more practical and user-friendly than online systems, but because they lack dynamic information, offline verification is regarded to be more difficult. Systems for verifying signatures are designed to determine if a particular signature is authentic (made by the claimed individual) or a forgery (produced by an impostor). The data collection, feature extraction, feature selection, and classification model make up the bulk of the suggested model. A convolutional neural network is used to extract features, and machine learning algorithms are used to verify handwritten signatures. To train CNN models for feature extraction and data augmentation, raw images of signatures are employed. VGG16, Inception-v3, Res-Net50, and Xception CNN architectures are employed. The recovered attributes are classified as authentic or false using Euclidean distance, cosine similarity, and supervised learning techniques such as Logistic Regression, Random Forest, SVM, and its variants. Data from ICDAR 2011, including pairwise-organized Signature Datasets, was used for testing. The database comprises the signatures of 69 different people.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pansare, A., Bhatia, S.: Handwritten signature verification using neural network. Int. J. Appl. Inform. Syst. 1(2), 44–49 (2012)
Hatkar, P.V., Salokhe, B.T., Malgave, A.A.: Offline handwritten signature verification using neural network. Methodology. 2(1), 1–5 (2015)
Kumar, P., Singh, S., Garg, A., Prabhat, N.: Hand written signature recognition & verification using neural network. Int. J. Adv. Res. Comput. Sci. Soft. Eng. 3(3) (2013)
Al-Omari, Y.M., Abdullah, S.N.H.S., Omar, K.: State-of-the-art in offline signature verification system. In: 2011 International Conference on Pattern Analysis and Intelligence Robotics, vol. 1, pp. 59–64. IEEE (2011)
Majhi, B., Santhosh Reddy, Y., Prasanna Babu, D.: Novel features for off-line signature verification. Int. J. Comput. Commun. Control. 1(1), 17–24 (2006)
Sisodia, K., Mahesh Anand, S.: Off-line handwritten signature verification using artificial neural network classifier. Int. J. Recent Trends Eng. 2(2), 205 (2009)
Kancharla, K., Kamble, V., Kapoor, M.: Handwritten signature recognition: a convolutional neural network approach. In: 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), pp. 1–5. IEEE (2018)
Jeiad, H.A.: Indian number handwriting features extraction and classification using multi-class SVM. Eng. Technol. J. 36(1A) (2018)
Jadhav, T.: Handwritten signature verification using local binary pattern features and KNN. Int. Res. J. Eng. Technol. (IRJET) 6(4), 579–586 (2019)
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)
Sanmorino, A., Yazid, S.: A survey for handwritten signature verification. In: 2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering, pp. 54–57. IEEE (2012)
Sam, S.M., Kamardin, K., Sjarif, N.N.A., Mohamed, N.: Offline signature verification using deep learning convolutional neural network (CNN) architectures GoogLeNet inception-v1 and inception-v3. Procedia Comput. Sci. 161, 475–483 (2019)
Gideon, S.J., Kandulna, A., Abhishek Kujur, A., Diana, A., Raimond, K.: Handwritten signature forgery detection using convolutional neural networks. Procedia Comput. Sci. 143, 978–987 (2018)
Yapici, M.M., Tekerek, A., Topaloglu, N.: Convolutional neural network based offline signature verification application. In: 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), pp. 30–34. IEEE (2018)
Hafemann, L.G., Sabourin, R., Oliveira, L.S.: Writer-independent feature learning for offline signature verification using deep convolutional neural networks. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2576–2583. IEEE (2016)
Mohapatra, R.K., Shaswat, K., Kedia, S.: Offline handwritten signature verification using CNN inspired by inception V1 architecture. In: 2019 Fifth International Conference on Image Information Processing (ICIIP), pp. 263–267. IEEE (2019)
Rana, T.S., Usman, H.M., Naseer, S.: Static handwritten signature verification using convolution neural network. In: 2019 International Conference on Innovative Computing (ICIC), pp. 1–6. IEEE (2019)
Shethwala, R., Pathar, S., Patel, T., Barot, P.: Transfer learning aided classification of lung sounds-wheezes and crackles. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1260–1266. IEEE (2021)
Sudharshan, D.P., Vismaya, R.N.: Handwritten signature verification system using deep learning. In: 2022 IEEE International Conference on Data Science and Information System (ICDSIS), pp. 1–5. IEEE (2022)
Tamrakar, P., Badholia, A.: Handwritten signature verification technology using deep learning–a review. In: 2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 813–817. IEEE (2022)
Mosaher, Q.S., Hasan, M.: Offline handwritten signature recognition using deep convolution neural network. Eur. J. Eng. Technol. Res. 7(4), 44–77 (2022)
Xiao, W., Ding, Y.: A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification. Symmetry 14(6), 1216 (2022)
Tsourounis, D., Theodorakopoulos, I., Zois, E.N., Economou, G.: From text to signatures: knowledge transfer for efficient deep feature learning in offline signature verification. Expert Syst. Appl. 189, 116136 (2022)
Thilakaraj, K., Uvaprasanth, S., Santha Perumal, T.: Signature verification using deep learning.
Hung, P.D., Bach, P.S., Vinh, B.T., Tien, N.H., Diep, V.T.: Offline handwritten signature forgery verification using deep learning methods. In: Zhang, Y.D., Senjyu, T., So-In, C., Joshi, A. (eds.) Smart Trends in Computing and Communications. Lecture Notes in Networks and Systems, vol. 396. Springer, Singapore (2023). https://doi.org/10.1007/978-981-16-9967-2_8
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Gosai, D., Vyas, S., Patel, S., Barot, P., Suthar, K. (2022). Handwritten Signature Verification Using Convolution Neural Network (CNN). In: Rajagopal, S., Faruki, P., Popat, K. (eds) Advancements in Smart Computing and Information Security. ASCIS 2022. Communications in Computer and Information Science, vol 1759. Springer, Cham. https://doi.org/10.1007/978-3-031-23092-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-23092-9_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23091-2
Online ISBN: 978-3-031-23092-9
eBook Packages: Computer ScienceComputer Science (R0)