Research On Signature Verification of Two-Tickets Based on Siamese Convolutional Neural Networks

The “two-tickets” system of electric power enterprises is an important organizational measure to ensure operation safety of electric power production site. In the actual electric power production, the operation safety accidents caused by “two tickets” signed not in accordance with regulations or by others occur from time to time. At present, electric power enterprises mainly rely on manual inspection to verify the authenticity of “two tickets” signature, which has the shortcomings of more human resources investment and low efficiency. In this paper, a signature identification method of “two-tickets” based on siamese convolutional neural networks is proposed. The proposed method introduces image processing, max-pooling, batch normalization, and dropout technologies, and effectively combines convolutional neural network and siamese network, which can effectively improve the accuracy of “two-tickets” signature identification. The results show that the proposed method is more accurate than k-nearest neighbor method, naive bayes method, decision tree method and support vector machine method. The proposed method can be applied to the “two-tickets” signature identification in electric power enterprises.