Handwritten Signature Recognition (HSR) is a vital task in document authentication and verification systems. This paper proposes a novel approach for offline HSR leveraging pre-trained Convolutional Neural Network (CNN) models. CNNs have demonstrated remarkable performance in various computer vision tasks, including image recognition, making them suitable for HSR tasks. Our proposed method uses pre-trained CNN models trained on large-scale image datasets, such as ImageNet, to extract high-level features from handwritten signature images. By fine-tuning these pre-trained models on a dataset of offline handwritten signatures, we aim to transfer the learned knowledge to the task of HSR. We explore different pre-trained CNN architectures, such as MobileNet, ShuffleNet, ResNet, and EfficientNet, and investigate their performance in HSR tasks. Furthermore, we propose a signature verification system that combines the features extracted from pre-trained CNN models with Euclidean Distance (ED) metric to authenticate handwritten signatures. Experimental results on benchmark datasets demonstrate the effectiveness of our proposed approach in achieving state-of-the-art performance in offline HSR tasks. 

Keywords - Handwritten Signature Recognition, Euclidean Distance, Pre-Trained CNN Architectures

[1] A. Sarkar and B. K. Singh, "A review on performance, security and various biometric template protection schemes for biometric authentication systems," Multimedia Tools and Applications, vol. 79, no. 37, pp. 27721-27776, 2020.

[2] M. Diaz, M. A. Ferrer, D. Impedovo, M. I. Malik, G. Pirlo, and R. Plamondon, "A perspective analysis of handwritten signature technology," Acm Computing Surveys (Csur), vol. 51, no. 6, pp. 1-39, 2019.

[3] E. N. Zois, M. Papagiannopoulou, D. Tsourounis, and G. Economou, "Hierarchical dictionary learning and sparse coding for static signature verification," in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2018, pp. 432-442.

[4] W. Hou, X. Ye, and K. Wang, "A survey of off-line signature verification," in 2004 International Conference on Intelligent Mechatronics and Automation, 2004. Proceedings., 2004: IEEE, pp. 536-541.

[5] E. J. Justino, F. Bortolozzi, and R. Sabourin, "A comparison of SVM and HMM classifiers in the off-line signature verification," Pattern recognition letters, vol. 26, no. 9, pp. 1377-1385, 2005.

[6] R. Ghosh, "A Recurrent Neural Network based deep learning model for offline signature verification and recognition system," Expert Systems with Applications, vol. 168, p. 114249, 2021.

[7] X. Han et al., "Pre-trained models: Past, present and future," AI Open, vol. 2, pp. 225-250, 2021.

[8] K. L. Elmore and M. B. Richman, "Euclidean distance as a similarity metric for principal component analysis," Monthly weather review, vol. 129, no. 3, pp. 540-549, 2001.

[9] A. B. Jagtap and R. S. Hegadi, "Offline handwritten signature recognition based on upper and lower envelope using eigen values," in 2017 World Congress on Computing and Communication Technologies (WCCCT), 2017: IEEE, pp. 223-226.

[10] A. Foroozandeh, A. A. Hemmat, and H. Rabbani, "Offline handwritten signature verification and recognition based on deep transfer learning," in 2020 International Conference on Machine Vision and Image Processing (MVIP), 2020: IEEE, pp. 1-7.

[11] Q. S.-u. Mosaher and M. Hasan, "Offline handwritten signature recognition using deep convolution neural network," European Journal of Engineering and Technology Research, vol. 7, no. 4, pp. 44-47, 2022.

[12] K. Daqrouq, H. Sweidan, A. Balamesh, and M. N. Ajour, "Off-line handwritten signature recognition by wavelet entropy and neural network," Entropy, vol. 19, no. 6, p. 252, 2017.

[13] H.-H. Kao and C.-Y. Wen, "An offline signature verification and forgery detection method based on a single known sample and an explainable deep learning approach," Applied Sciences, vol. 10, no. 11, p. 3716, 2020.

[14] G. Ramesh, G. N. Sharma, J. M. Balaji, and H. Champa, "Offline Kannada handwritten character recognition using convolutional neural networks," in 2019 IEEE international wie conference on electrical and computer engineering (WIECON-ECE), 2019: IEEE, pp. 1-5.

[15] N. Sharma et al., "Offline signature verification using deep neural network with application to computer vision," Journal of Electronic Imaging, vol. 31, no. 4, pp. 041210-041210, 2022.

[16] A. G. Howard et al., "Mobilenets: Efficient convolutional neural networks for mobile vision applications," arXiv preprint arXiv:1704.04861, 2017.

[17] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.

[18] M. Tan and Q. Le, "Efficientnet: Rethinking model scaling for convolutional neural networks," in International conference on machine learning, 2019: PMLR, pp. 6105-6114.

[19] X. Zhang, X. Zhou, M. Lin, and J. Sun, "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices," in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 18-23 June 2018 2018, pp. 6848-6856, doi: 10.1109/CVPR.2018.00716.

[20] M. Hussain, "YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection," Machines, vol. 11, no. 7, p. 677, 2023.

[21] D. Engin, A. Kantarci, S. Arslan, and H. K. Ekenel, "Offline signature verification on real-world documents," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, 2020, pp. 808-809.

[22] D. Gumusbas and T. Yildirim, "Offline signature identification and verification using capsule network," in 2019 IEEE international symposium on innovations in intelligent systems and applications (INISTA), 2019: IEEE, pp. 1-5.

[23] I. Vasilev, D. Slater, G. Spacagna, P. Roelants, and V. Zocca, Python Deep Learning: Exploring deep learning techniques and neural network architectures with Pytorch, Keras, and TensorFlow. Packt Publishing Ltd, 2019.

[24] D. L. Olson and D. Delen, Advanced data mining techniques. Springer Science & Business Media, 2008.