2023/08/01 by Fu-Hsien Huang, Huang, Fu-Hsien, Hsin‐Min Lu +1 · 2 citations
Computer Science · Engineering · #Computer Science and Engineering #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Handwritten Text Recognition Techniques #Vehicle License Plate Recognition
paper · pdf · doi:10.48550/arxiv.2308.00428
openalex publication_date 2023/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Handwritten signature verification, crucial for legal and financial institutions, faces challenges including inter-writer similarity, intra-writer variations, and limited signature samples. To address these, we introduce the MultiScale Signature feature learning Network (MS-SigNet) with the co-tuplet loss, a novel metric learning loss designed for offline handwritten signature verification. MS-SigNet learns both global and regional signature features from multiple spatial scales, enhancing feature discrimination. This approach effectively distinguishes genuine signatures from skilled forgeries by capturing overall strokes and detailed local differences. The co-tuplet loss, focusing on multiple positive and negative examples, overcomes the limitations of typical metric learning losses by addressing inter-writer similarity and intra-writer variations and emphasizing informative examples. The code is available at https://github.com/ashleyfhh/MS-SigNet. We also present HanSig, a large-scale Chinese signature dataset to support robust system development for this language. The dataset is accessible at https://github.com/hsinmin/HanSig. Experimental results on four benchmark datasets in different languages demonstrate the promising performance of our method in comparison to state-of-the-art approaches.