@misc{Wang_Xuyang_Research,
 author={Wang, Xuyang and Xu, Chengzhi and Dong, Liuyuan and Xie, Ruizhen and Yang, Wanli},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 abstract={With the rise of the digital era, handwriting examination has become crucial for identity verification and document provenance. However, determining whether samples from different texts are written by the same person remains challenging. The challenge is greater in few-shot settings, where data are scarce and writing styles vary widely. Traditional methods often lack sufficient accuracy and robustness.},
 abstract={We propose a dual-branch Siamese network for handwriting verification. It fuses attention mechanisms with a feature-bank matching strategy. This design improves adaptation and generalization under few-shot conditions. It also suppresses background noise and emphasizes key writing traits. We evaluate the method on CCSbC, the mixed Chinese-English hard-pen dataset MDC, and CCD-CQU. The model attains high accuracy on multi-class few-shot classification tasks. It shows strong robustness and adaptability. With data augmentation and feature optimization, it could deliver more efficient handwriting identification in real-world applications},
 title={Research on few-shot handwriting identification based on Siamese networks},
 type={artykuł},
 keywords={handwriting identification, attention mechanism, Siamese network, feature library, few-shot learning},
}