2022/03/17 by Xiaotong Ji, Yan Zheng, Ji, Xiaotong +5
Computer Science · Engineering · Mathematics · #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Data mining #Documentation #Engineering #FOS: Computer and information sciences #Handwritten Text Recognition Techniques #Machine Learning (cs.LG) #Machine learning #Mathematics #Pairing #Pattern recognition (psychology) #Rank (graph theory) #Reliability (semiconductor) #Signature (topology) #Task (project management) #Text and Document Classification Technologies #Topic Modeling #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2203.09927
arxiv created 2022/03/17 · openalex publication_date 2022/03/17 · arxiv updated 2022/03/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Signature verification, as a crucial practical documentation analysis task, has been continuously studied by researchers in machine learning and pattern recognition fields. In specific scenarios like confirming financial documents and legal instruments, ensuring the absolute reliability of signatures is of top priority. In this work, we proposed a new method to learn "top-rank pairs" for writer-independent offline signature verification tasks. By this scheme, it is possible to maximize the number of absolutely reliable signatures. More precisely, our method to learn top-rank pairs aims at pushing positive samples beyond negative samples, after pairing each of them with a genuine reference signature. In the experiment, BHSig-B and BHSig-H datasets are used for evaluation, on which the proposed model achieves overwhelming better pos@top (the ratio of absolute top positive samples to all of the positive samples) while showing encouraging performance on both Area Under the Curve (AUC) and accuracy.