Open Access

ARTICLE

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

by Jianfeng Lu^1,2, Xiangye Huang¹, Caijin Li¹, Renlin Xin¹, Shanqing Zhang^1,2, Mahmoud Emam^1,2,3,*

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

* Corresponding Author: Mahmoud Emam. Email:

(This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)

Computer Modeling in Engineering & Sciences 2025, 142(1), 405-423. https://doi.org/10.32604/cmes.2024.058121

Received 04 September 2024; Accepted 23 November 2024; Issue published 17 December 2024

Abstract

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 labor-intensive 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) and Visual Geometry Group 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 a multi-stage seal recognition algorithm based 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.

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Keywords

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