基于神经网络的印章盖印时间识别的研究
发布时间：2018-11-20 08:33
【摘要】： 随着各种经济、民事案件中利用伪造文件制成时间进行违法犯罪活动的增多,鉴定文件制成时间成为文检人员急待解决的问题。作为文件真实有效性凭据之一的印章印文,不再仅仅涉及同一认定的问题,越来越多的争议是关于盖印时间,也即盖印时间是否与文件所标称的时间相一致的问题。根据印章印文可变性印迹特征鉴别印章的盖印时间,是解决此类问题的有效途径之一。 另一方面,由于计算机技术的飞跃发展,人工神经网络在数据挖掘领域的广泛应用,本文提出应用BP神经网络技术对文检专家识别出的经验数据进行挖掘,最终实现印章印文盖印时间的辅助识别。本文的主要研究内容如下: 首先,提出了基于专家经验的印章特征值指标体系。通过文检专家识别的印文特征,分析出影响印章盖印时间识别的重要的可变性印迹特征,对这些特征进行分析与汇总,得到一套科学合理的特征值指标体系。 其次,在建立基于专家经验的印章特征值指标体系的基础上,对特征值进行量化处理,将定性的印文特征转换为定量的特征,为BP神经网络的应用提供合理可靠的数据支持。 最后,论文分析了BP神经网络进行印章印文盖印时间识别的原理,利用三层前馈神经网络建立识别模型,详细探讨了网络的拓扑结构、隐含层节点个数确定的原则、样本数据的选取和预处理、初始参数的确定、激活函数的选取等问题。用C#语言实现了改进的BP学习算法。以某一公司、某一类型的印章为例,建立对应的印章盖印时间识别的神经网络模型。通过样本数据以及测试数据的仿真实验表明,该模型能够满足高精度的要求,具有较好的泛化能力。通过将该模型应用到印章盖印时间识别领域,实现了印章盖印时间识别的科学化和自动化,同时表明了应用BP神经网络识别印章盖印时间的有效性和实用价值。
[Abstract]:With all kinds of economy, the use of forged documents to make time for criminal activities increased in civil cases, and the time of making identification documents has become an urgent problem to be solved by document inspectors. As one of the evidences of the true validity of the document, the seal is no longer involved in the same issue, more and more controversy is about the time of seal, that is, whether the time of seal is consistent with the nominal time of document. One of the effective ways to solve this problem is to identify the time of seal according to the feature of variability imprinting of seal print. On the other hand, due to the rapid development of computer technology and the wide application of artificial neural network in the field of data mining, this paper proposes to use BP neural network technology to mine the empirical data identified by document inspection experts. Finally, the identification of seal time is realized. The main contents of this paper are as follows: firstly, a seal characteristic value index system based on expert experience is proposed. By analyzing the features of print recognized by document inspection experts, this paper analyzes the important variable imprinting features that affect the recognition of seal, analyzes and summarizes these features, and obtains a set of scientific and reasonable characteristic value index system. Secondly, based on the establishment of the seal characteristic value index system based on the expert experience, the characteristic value is quantified, and the qualitative print feature is converted into the quantitative feature, which provides reasonable and reliable data support for the application of BP neural network. Finally, the paper analyzes the principle of BP neural network for seal time recognition, establishes the recognition model by using three-layer feedforward neural network, and discusses in detail the topological structure of the network and the principle of determining the number of hidden layer nodes. The selection and preprocessing of sample data, the determination of initial parameters, the selection of activation function and so on. The improved BP learning algorithm is implemented in C # language. Taking the seal of a certain company and a certain type of seal as an example, a neural network model for the identification of seal time is established. The simulation results of sample data and test data show that the model can meet the requirement of high precision and has better generalization ability. By applying the model to the field of seal time recognition, the scientific and automatic identification of seal time is realized, and the validity and practical value of BP neural network in identifying seal time are also demonstrated.
【学位授予单位】：大连海事大学
【学位级别】：硕士
【学位授予年份】：2009
【分类号】：D918.91
本文编号：2344412
[Abstract]:With all kinds of economy, the use of forged documents to make time for criminal activities increased in civil cases, and the time of making identification documents has become an urgent problem to be solved by document inspectors. As one of the evidences of the true validity of the document, the seal is no longer involved in the same issue, more and more controversy is about the time of seal, that is, whether the time of seal is consistent with the nominal time of document. One of the effective ways to solve this problem is to identify the time of seal according to the feature of variability imprinting of seal print. On the other hand, due to the rapid development of computer technology and the wide application of artificial neural network in the field of data mining, this paper proposes to use BP neural network technology to mine the empirical data identified by document inspection experts. Finally, the identification of seal time is realized. The main contents of this paper are as follows: firstly, a seal characteristic value index system based on expert experience is proposed. By analyzing the features of print recognized by document inspection experts, this paper analyzes the important variable imprinting features that affect the recognition of seal, analyzes and summarizes these features, and obtains a set of scientific and reasonable characteristic value index system. Secondly, based on the establishment of the seal characteristic value index system based on the expert experience, the characteristic value is quantified, and the qualitative print feature is converted into the quantitative feature, which provides reasonable and reliable data support for the application of BP neural network. Finally, the paper analyzes the principle of BP neural network for seal time recognition, establishes the recognition model by using three-layer feedforward neural network, and discusses in detail the topological structure of the network and the principle of determining the number of hidden layer nodes. The selection and preprocessing of sample data, the determination of initial parameters, the selection of activation function and so on. The improved BP learning algorithm is implemented in C # language. Taking the seal of a certain company and a certain type of seal as an example, a neural network model for the identification of seal time is established. The simulation results of sample data and test data show that the model can meet the requirement of high precision and has better generalization ability. By applying the model to the field of seal time recognition, the scientific and automatic identification of seal time is realized, and the validity and practical value of BP neural network in identifying seal time are also demonstrated.
【学位授予单位】：大连海事大学
【学位级别】：硕士
【学位授予年份】：2009
【分类号】：D918.91
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1 张金源;基于神经网络的印章盖印时间识别的研究[D];大连海事大学;2009年
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