Title: Offline handwritten signature recognition based on generative adversarial networks

Authors: Xiaoguang Jiang

Addresses: Department of Culture and Arts, Yongcheng Vocational College, Yongcheng, 476600, China

Abstract: In order to shorten the time for offline handwritten signature recognition and reduce the probability of false positives, an offline handwritten signature recognition method based on generative adversarial networks is proposed. Firstly, select pen pressure, pen tilt angle, pen azimuth angle, and multi-level velocity moment as the main dynamic features of offline handwritten signatures, and calculate the Pearson correlation coefficients of these dynamic features. Secondly, calculate and sum multiple features to complete the dynamic feature selection and fusion of offline handwritten signatures. Finally, using dynamic feature fusion data as input and offline handwritten signature recognition results as output, a generative adversarial network model is constructed to complete the recognition of offline handwritten signatures. Experimental results show that this method can complete the recognition of 200 offline handwritten signatures in 0.66 seconds, with an error rejection rate and error acceptance rate of only 1%, and a recognition accuracy rate of 95%.

Keywords: dynamic features; offline handwriting; signature recognition; Pearson coefficient; adversarial neural network; convolutional neural network; CNN.

DOI: 10.1504/IJBM.2024.138227

International Journal of Biometrics, 2024 Vol.16 No.3/4, pp.236 - 255

Received: 19 May 2023
Accepted: 25 Jul 2023
Published online: 30 Apr 2024 *

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