Abstract
Based on convolutional neural network, a simpler network for offline handwritten signature recognition is proposed in this paper. A total of 7,200 handwritten signature images in Chinese, Uyghur, and Kazakh languages were collected from 300 volunteers to establish an offline multi-lingual handwritten signature database. The signature images were randomly divided into training, verification, and testing sets. The average accuracy of five experiments was taken as the recognition result. Because it is difficult to collect a large number of signatures at one time, many experiments have been carried out on a multi-lingual signature database and CEDAR database with the training samples are 6, 12, and 18 respectively. Experimental results show that this method is an effective signature recognition method, and the effect is better than the existing network when there are few training samples.
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References
Sun, Z., Li, Q., Liu, Y., Zhu, Y.: Opportunities and Challenges for Biometrics (2021)
Tanwar, S., Obaidat, M.S., Tyagi, S., Kumar, N.: Online Signature-Based Biometric Recognition (2019)
Das, S.D., Ladia, H., Kumar, V., Mishra, S.: Writer Independent Offline Signature Recognition Using Ensemble Learning (2019)
Ubul, K., Wang, X., Yimin, A., Zhang, S., Yibulayin, T.: Multilingual offline handwritten signature recognition based on statistical features. In: Zhou, J., et al. (eds.) CCBR 2018. LNCS, vol. 10996, pp. 727–735. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-97909-0_77
Mahpirat, L.M., Zhu, Y., Mamat, H., Ubul, K.: Off-Line Handwritten Signature Recognition Based on Discrete Curvelet Transform (2019)
Ubul, K., Zhu, Y., Mamut, M., Yadikar, N., Yibulayin, T.: Uyghur Off-Line Signature Recognition Based on Local Central Line Features (2017)
Taskiran, M., Cam, Z.G.: Offline signature identification via HOG features and artificial neural networks. In: 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE (2017)
Hadjadji, B., Chibani, Y., Nemmour, H.: An efficient open system for offline handwritten signature identification based on curvelet transform and one-class principal component analysis. Neurocomputing 265, 66–77 (2017)
Calik, N., Kurban, O.C., Yilmaz, A.R., Yildirim, T., Ata, L.D.: Large-scale offline signature recognition via deep neural networks and feature embedding. Neurocomputing 359, 1–14 (2019)
Rezaei, M., Naderi, N.: Persian Signature Verification using Fully Convolutional Networks (2019)
Poddar, J., Parikh, V., Bharti, S.K.: Offline signature recognition and forgery detection using deep learning - ScienceDirect. Procedia Comput. Sci. 170, 610–617 (2020)
Hafemann, L.G., Sabourin, R., Oliveira, L.S.: Learning features for offline handwritten signature verification using deep convolutional neural networks. Patt. Recogn. 70, 163–176 (2017)
Ooi, S.Y., Teoh, A.B.J., Pang, Y.H., Hiew, B.Y.: Image-based handwritten signature verification using hybrid methods of discrete Radon transform, principal component analysis and probabilistic neural network. Appl. Soft Comput. 40C , 274–282 (2016)
Ghosh, R.: A Recurrent Neural Network based deep learning model for offline signature verification and recognition system. Expert Syst. Appl. 168(5) (2020)
Srihari, S.N., Xu, A., Kalera, M.K.: Learning strategies and classification methods for off-line signature verification. In: Ninth International Workshop on Frontiers in Handwriting Recognition IEEE (2004)
Tajbakhsh, N., Suzuki, K.: Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs. Patt. Recogn. 63, 476–486 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Sci. (2014)
Loffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, PMLR, vol. 37, pp. 448–456 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. CVPR IEEE Computer Society (2015)
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Li, W., Mahpirat, Xu, X., Aysa, A., Ubul, K. (2022). A Simple Convolutional Neural Network for Small Sample Multi-lingual Offline Handwritten Signature Recognition. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_40
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DOI: https://doi.org/10.1007/978-3-031-20233-9_40
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