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. 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 named 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.
Wang et al. (Thu,) studied this question.