Transformer and Residual Connection based Signet for Offline Signatures Verification

We propose two enhancements to the Signet model for offline signature verification. While Signet performs well on Bengali, Hindi, and UTSig datasets, our modifications address its limitations on others. TranReSignet-16-128 outperforms Signet on the CEDAR dataset, and ReSignet improves AUC by 6% on GPDS-150. These adjustments demonstrate that tailored models can enhance accuracy and efficiency, offering effective alternatives for datasets where Signet under performs.

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Publication Details

Published
2025-05-23
DOI
https://doi.org/10.1109/icaibd64986.2025.11081982
Primary Topic
Power Systems and Technologies
Type
article
Field-Weighted Citation Impact
0.00

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article

Transformer and Residual Connection based Signet for Offline Signatures Verification

Yuheng Guo, Hiroyuki Satō
Power Systems and Technologies
article

Transformer and Residual Connection based Signet for Offline Signatures Verification

Yuheng Guo, Hiroyuki Satō
article en

Abstract

We propose two enhancements to the Signet model for offline signature verification. While Signet performs well on Bengali, Hindi, and UTSig datasets, our modifications address its limitations on others. TranReSignet-16-128 outperforms Signet on the CEDAR dataset, and ReSignet improves AUC by 6% on GPDS-150. These adjustments demonstrate that tailored models can enhance accuracy and efficiency, offering effective alternatives for datasets where Signet under performs.

Tokyo University of Information Sciences (JP), National Institute of Informatics (JP)
University of Tokyo
Openalex Percentile: Top 84%
Power Systems and Technologies
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