高仿真光敏印章盖印印文的自动识别

为了实现高仿真光敏印章印文的自动识别，探究训练样本量、网络模型对识别准确率的影响，通过扫描打印伪造法、拓印设计伪造法制备2枚高仿光敏印章，盖印3 000枚印文作为训练样本，30枚印文作为测试样本，利用卷积神经网络4种模型实现高仿真光敏印章印文的鉴别。4种网络模型均能得到100%的识别准确率。仿真实验结果表明，针对高仿真光敏印章印文识别任务，卷积神经网络能作为一种可行的方法为检验提供辅助参考；综合分析4种网络模型，Resnet50是最优选择。

Automatic recognition of high-simulation photosensitive seal stamping

To realize the automatic recognition of high simulation photosensitive seals and explore the influence of training sample size and network model on the recognition accuracy, two high imitation photosensitive seals were prepared by scanning, printing and rubbing design forgery. 3 000 seals were stamped as training samples and 30 seals were stamped as test samples. Four models of convolutional neural network were utilized to identify the high simulation photosensitive seals. According to the results, all the four network models can get 100% recognition accuracy. The convolutional neural network can be used as a feasible method to provide auxiliary reference for the test. By comprehensive analyzing the four network models, Resnet50 is the best choice for the task of high-simulation photosensitive seal printing.