Scribe Verification in Chinese manuscripts using Siamese, Triplet, and Vision Transformer Neural Networks
The paper examines deep learning models for scribe verification in Chinese manuscripts. That is, to automatically determine whether two manuscript fragments were written by the same scribe using deep metric learning methods. Two datasets were used: the Tsinghua Bamboo Slips Dataset and a selected subset of the Multi-Attribute Chinese Calligraphy Dataset, focusing on the calligraphers with a large number of samples. Siamese and Triplet neural network architectures are implemented, including convolutional and Transformer-based models. The experimental results show that the MobileNetV3+ Custom Siamese model trained with contrastive loss achieves either the best or the second-best overall acc...
| Authors | Dimitrios-Chrysovalantis Liakopoulos, Yanbo Zhang, Chongsheng Zhang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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