| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
