Multi-Stage-Based Siamese Neural Network for Seal Image Recognition  

作　　者：Jianfeng Lu Xiangye Huang Caijin Li Renlin Xin Shanqing Zhang Mahmoud Emam 

机构地区：[1]School of Computer Science and Technology,Hangzhou Dianzi University,Hangzhou,310018,China [2]Shangyu Institute of Science and Engineering,Hangzhou Dianzi University,Shaoxing,312300,China [3]Faculty of Artificial Intelligence,Menoufia University,Shebin El-Koom,32511,Egypt

出　　处：《Computer Modeling in Engineering & Sciences》2025年第1期405-423,共19页工程与科学中的计算机建模（英文）

基　　金：the National Natural Science Foundation of China(Grant No.62172132);Public Welfare Technology Research Project of Zhejiang Province(Grant No.LGF21F020014);the Opening Project of Key Laboratory of Public Security Information Application Based on Big-Data Architecture,Ministry of Public Security of Zhejiang Police College(Grant No.2021DSJSYS002).

摘　　要：Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting.Stamped seal inspection is commonly audited manually to ensure document authenticity.However,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the seal.Traditional image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image recognition.However,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp datasets.Additionally,the fixed training data categories make handling new categories to be a challenging task.This paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these limitations.Firstly,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese network.Finally,we compare the results with the pre-stored standard seal template images in the database to obtain the seal type.To evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in total.The proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation processes.Furthermore,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.

关 键 词：Seal recognition seal authentication document tampering siamese network spatial transformer network similarity comparison network 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]