Sig-RFPNet: multi-branch network and multi-classifier method for offline signature recognition
Author:
Funder
Xinjiang University
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11760-024-03433-5.pdf
Reference29 articles.
1. Wang, Z., Muhammat, M., Yadikar, N., Aysa, A., Ubul, K.: Advances in offline handwritten signature recognition research: a review. IEEE Access 11, 120222–120236 (2023)
2. Kiran, P., Divakarachari, P.B., Yashwanth, J., Bharath, K.N.: Offline signature recognition using image processing techniques and back propagation neuron network system. SN Comput. Sci. 2(3), 196 (2021)
3. Roy, S., Sarkar, D., Malakar, S., Sarkar, R.: Offline signature verification system: a graph neural network based approach. J. Ambient Intell. Humaniz. Comput. 14(7), 8219–8229 (2023)
4. Hameed, M.M., Ahmad, R., Kiah, M.L.M., Murtaza, G.: Machine learning-based offline signature verification systems: a systematic review. Signal Process. Image Commun. 93, 116139 (2021)
5. Shen, Q., Luan, F., Yuan, S.: Multi-scale residual based siamese neural network for writer-independent online signature verification. Appl. Intell. 52(12), 14571–14589 (2022)
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