用深度度量学习识别古籍笔迹是否出自同一书手
Scribe Verification in Chinese manuscripts using Siamese, Triplet, and Vision Transformer Neural Networks
- 设计定制的Siamese网络结合MobileNetV3与对比损失
- 在两大数据集上达到最优或次优准确率与AUC
- 适合古籍数字化与书法鉴定研究者参考
本文研究中文古籍手稿中书手验证的深度学习模型，即通过深度度量学习自动判断两段手稿是否由同一书手书写。实验使用了清华大学竹简数据集和多属性中文书法数据集的一个子集，聚焦于样本量较大的书法家。实现了基于卷积和Transformer的Siamese与三元组神经网络架构。结果表明，采用对比损失训练的MobileNetV3+自定义Siamese模型在两个数据集上均取得最佳或第二好的整体准确率与受试者工作特征曲线下面积（AUC）。
原文摘要 · Abstract (English)
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 accuracy and area under the Receiver Operating Characteristic Curve on both datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。