Abstract
To reduce fraud in financial transactions, signature verification is important for security purposes. In this paper, an attempt has been made to analysis the performance of off-line handwritten signature verification using image-based features. Photocopies and scanned documents are considered as the best possible evidence in the situations when the original documents are either lost or damaged. Although the photocopies are the filtered images of original information and do not reproduce details as in the original documents. In this paper, combinations of four features, i.e., Average object area, mean, Euler number and area of signature image is used to verify the signature. Publically available database BHsig260 is used. In this database, two types of signature are available, i.e.,Bengali and Hindi. Proposed work shows that accuracy of Hindi off-line signature verification is 78.5% with sample size of 15 and accuracy of Bengali off-line signature verification is 69.1 with sample size of 20.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
References
K. Kumari, V.K. Shrivastava, Factors affecting the accuracy of automatic signature verification, IEEE (2016)
K. Kumari, V.K. Shrivastava, A review of automatic signature verification, in ICTCS (2016)
K. Kumari, S. Rana, Writer-independent off-line signature verification. Int. J. Comput. Eng. Technol. (IJCET). 9(4), 85–89 (2018)
K. Kumari, S. Rana, Offline signature verification using intelligent algorithm. Int. J. Eng. Technol. [S.l.]. 7(4.12), 69–72 (2018). ISSN 2227-524X
S. Pal, A. Alaei, U. Pal, M. Blumenstein, Off-line Bangla signature verification: an empirical study, in The 2013 International Joint Conference on Neural Networks (IJCNN), IEEE (2013), pp. 1–7
S. Pal, V. Nguyen, M. Blumenstein, U. Pal, Off-line Bangla signature verification, in 2012 10th IAPR International Workshop on Document Analysis Systems (DAS), IEEE (2012), pp. 282–286
S. Pal, M. Blumenstein, U. Pal, Hindi off-line signature verification, in 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR), IEEE (2012), pp. 373–378
S. Pal, A. Alaei, U. Pal, M. Blumenstein, Performance of an off-line signature verification method based on texture features on a large indic-script signature dataset, in 2016 12th IAPR Workshop on Document Analysis Systems (DAS), IEEE (2016, April), pp. 72–77
S. Dey, A. Dutta, J.I. Toledo, S.K. Ghosh, J. Lladós, U. Pal, SigNet: convolutional siamese network for writer independent offline signature verification. arXiv preprint arXiv:1707.02131 (2017)
B.S. Thakare, H.R. Deshmukh, A combined feature extraction model using SIFT and LBP for offline signature verification system, in 2018 3rd International Conference for Convergence in Technology (I2CT), IEEE (2018), pp. 1–7
M.B. Yilmaz, O.Z.T. Kagan, Hybrid user-independent and user-dependent offline signature verification with a two-channel CNN, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE (2018), pp. 639–6398
V.L. Souza, A.L. Oliveira, R. Sabourin, A writer-independent approach for offline signature verification using deep convolutional neural networks features, in 2018 7th Brazilian Conference on Intelligent Systems (BRACIS), IEEE (2018), pp. 212–217
Acknowledgements
The authors are grateful to the anonymous reviewers for their constructive comments which helped to improve this paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rana, S., Sharma, A., Kumari, K. (2020). Performance Analysis of Off-Line Signature Verification. In: Khanna, A., Gupta, D., Bhattacharyya, S., Snasel, V., Platos, J., Hassanien, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1087. Springer, Singapore. https://doi.org/10.1007/978-981-15-1286-5_14
Download citation
DOI: https://doi.org/10.1007/978-981-15-1286-5_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1285-8
Online ISBN: 978-981-15-1286-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)