Affiliation:
1. College of Information Science and Engineering, Hohai University, Changzhou 213022, China
2. School of Microelectronics, South China University of Technology, Guangzhou 511442, China
Abstract
This study proposes Stamp-MLP, an enhanced seal impression representation learning technique based on MLP-Mixer. Instead of using the patch linear mapping preprocessing method, this technique uses circular seal remapping, which reserves the seals’ underlying pixel-level information. In the proposed Stamp-MLP, the average pooling is replaced by a global pooling of attention to extract the information more comprehensively. There were three classification tasks in our proposed method: categorizing the seal surface, identifying the product type, and distinguishing individual seals. The three tasks shared an identical dataset comprising 81 seals, encompassing 16 distinct seal surfaces, with each surface featuring six diverse product types. The experiment results showed that, in comparison to MLP-Mixer, VGG16, and ResNet50, the proposed Stamp-MLP achieved the highest classification accuracy (89.61%) in seal surface classification tasks with fewer training samples. Meanwhile, Stamp-MLP outperformed the others with accuracy rates of 90.68% and 91.96% in the product type and seal impression classification tasks, respectively. Moreover, Stamp-MLP had the fewest model parameters (2.67 M).
Funder
National Natural Science Foundation of China
Guangdong Basic and Applied Basic Research Foundation
the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province
Key Research and Development Program of Jiangsu Province
Postdoctoral Science Foundation of Jiangsu Province
Subject
General Physics and Astronomy