Abstract
Automated approach for human identification based on biometric traits has become popular research topic among the scientists since last few decades. Among the several biometric modalities, handwritten signature is one of the very common and most prevalent approaches. In the past, researchers have proposed different handcrafted feature-based techniques for automatic writer identification from offline signatures. Currently huge interests towards deep learning-based solutions for several real-life pattern recognition problems have been found which revealed promising results. In this paper, we propose a light-weight CNN architecture to identify writers from offline signatures written by two popular scripts namely Devanagari and Roman. Experiments were conducted using two different frameworks which are as follows: (i) In first case, signature script separation has been carried out followed by script-wise writer identification, (ii) Secondly, signature of two scripts was mixed together with various ratios and writer identification has been performed in a script independent manner. Outcome of both the frameworks have been analyzed to get the comparative idea. Furthermore, comparative analysis was done with recognized CNN architectures as well as handcrafted feature-based approaches and the proposed method shows better outcome. The dataset used in this paper can be freely downloaded from the link: https://ieee-dataport.org/open-access/multi-script-handwritten-signature-roman-devanagari for research purpose.
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
References
Diaz, M., Ferrer, M.A., Impedovo, D., Malik, M.I., Pirlo, G., Plamondon, R.: A perspective analysis of handwritten signature technology. ACM Comput. Surv. 51(6) (2019). https://doi.org/10.1145/3274658. Article No. 117
https://rajbhasha.gov.in/en/languages-included-eighth-schedule-indian-constitution. Accessed 20 Feb 2021
Obaidullah, S.M., Goswami, C., Santosh, K.C., Halder, C., Das, N., Roy, K.: Separating Indic scripts with ‘matra’ as a precursor for effective handwritten script identification in multi-script documents. Int. J. Pattern Recogn. Artif. Intell. (IJPRAI) 31(4), 1753003 (17 pages) (2017). World Scientific
Nguyen, V., Blumenstein, M., Leedham, G.: Global features for the off-line signature verification problem. In: 10th International Conference on Document Analysis and Recognition (ICDAR 2009), pp. 1300–1304 (2009)
Schafer, B., Viriri, S.: An off-line signature verification system. In: International Conference on Signal and Image Processing Applications (ICSIPA 2009), 95–100 (2009)
Vargas, J.F., Ferrer, M.A., Travieso, C.M., Alonso, J.B.: Off-line signature verification based on grey level information using texture features. Pattern Recogn. 44(2), 375–385 (2011)
Serdouk, Y., Nemmour, H., Chibani, Y.: Handwritten signature verification using the quad-tree histogram of templates and a support vector-based artificial immune classification. Image Vis. Comput. 66(2017), 26–35 (2017)
Malik, M.I., Liwicki, M., Dengel, A., Uchida, S., Frinken, V.: Automatic signature stability analysis and verification using local features. In 14th International Conference on Frontiers in Handwriting Recognition (ICFHR 2014), pp. 621–626. IEEE (2014)
Deng, H.-R., Wang, Y.-H.: On-line signature verification based on correlation image. In: International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1788–1792 (2009)
Falahati, D., Helforoush, M.S., Danyali, H., Rashidpour, M.: Static signature verification for Farsi and Arabic signatures using dynamic time warping. In: 19th Iranian Conference on Electrical Engineering (ICEE 2011), pp. 1–6 (2011)
Mamoun, S.: Off-line Arabic signature verification using geometrical features. In: National Workshop on Information Assurance Research, pp. 1–6 (2016)
Darwish, S., El-Nour, A.: Automated offline Arabic signature verification system using multiple features fusion for forensic applications. Arab J. Forensic Sci. Forensic Med. 1(4), 424–437 (2016)
Dutta, A., Pal, U., Lladós, J.: Compact correlated features for writer independent signature verification. In: 23rd International Conference on Pattern Recognition (ICPR), Cancún Center, Cancún, México, 4–8 December 2016 (2016)
Pal, S., Pal, U., Blumenstein, M.: Off-line verification technique for Hindi signatures. IET Biometr. 2(4), 182–190 (2013)
Pal, S., Reza, A., Pal, U., Blumenstein, M.: SVM and NN based offline signature verification. Int. J. Comput. Intell. Appl. 12(4), 1340004 (2013)
Pal, S., Alaei, A., Pal, U., Blumenstein, M.: Performance of an off-line signature verification method based on texture features on a large Indic-script signature dataset. In: 12th IAPR Workshop on Document Analysis Systems (DAS 2016), pp. 72–77. IEEE (2016)
Dey, S., Dutta, A., Toledo, J.I., Ghosh, S.K., Lladós, J., Pal, U.: SigNet: convolutional siamese network for writer independent offline signature verification. Corr (2017). arXiv:1707.02131
Soleimani, A., Araabi, B.N., Fouladi, K.: Deep multitask metric learning for offline signature verification. Pattern Recogn. Lett. 80(2016), 84–90 (2016)
Zhang, Z., Liu, X., Cui, Y.: Multi-phase offline signature verification system using deep convolutional generative adversarial networks. In: 9th International Symposium on Computational Intelligence and Design (ISCID 2016), vol. 2, pp. 103–107 (2016)
Wang, G.D., Zhang, P.L., Ren, G.Q., Kou, X.: Texture feature extraction method fused with LBP and GLCM. Comput. Eng. 38, 199–201 (2012)
Sharma, N., Chanda, S., Pal, U., Blumenstein, M.: Word-wise script identification from video frames. ICDAR 2013, 867–871 (2013)
Chen, J., Shan, S., He, C., et al.: WLD: a robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1705–1720 (2010)
Jolliffe I., Principal component analysis. In: Lovric, M. (eds.) International Encyclopedia of Statistical Science. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-04898-2_455
Obaidullah, S.M., Santosh, K.C., Halder, C., Das, N., Roy, K.: Automatic Indic script identification from handwritten documents: page, block, line and word-level approach. Int. J. Mach. Learn. Cybern. 10(1), 87–106 (2019)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. arXiv preprint. arXiv:1801.04381 (2018)
Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks (2019). https://arxiv.org/pdf/1905.11946.pdf
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2015)
Jain, A., Singh, S.K., Singh, K.P.: Handwritten signature verification using shallow convolutional neural network. Multimed. Tools Appl. 79, 19993–20018 (2020)
Gideona, S.J., Kandulna, A., Abhishek, A., Diana, K.A., Raimond, K.: Handwritten signature forgery detection using convolutional neural networks. Procedia Comput. Sci. 143(2018), 978–987 (2018)
Gabor Wavelet Transform. https://bit.ly/3bl03Sx. Accessed 10 May 2021
Acknowledgement
The first author of this paper is thankful to Science and Engineering Research Board (SERB), DST, Govt. of India for funding this research through Teachers Associateship for Research Excellence (TARE) grant TAR/2019/000273.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Obaidullah, S.M., Ghosh, M., Mukherjee, H., Roy, K., Pal, U. (2021). Automatic Signature-Based Writer Identification in Mixed-Script Scenarios. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12822. Springer, Cham. https://doi.org/10.1007/978-3-030-86331-9_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-86331-9_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86330-2
Online ISBN: 978-3-030-86331-9
eBook Packages: Computer ScienceComputer Science (R0)
