Scribe Verification in Chinese manuscripts using Siamese, Triplet, and Vision Transformer Neural Networks

TL;DR

This paper explores deep learning techniques, including Siamese, Triplet, and Vision Transformer models, to verify if Chinese manuscript fragments are by the same scribe, achieving high accuracy on two datasets.

Contribution

It introduces and compares multiple neural network architectures, including Transformer-based models, for scribe verification in Chinese manuscripts, demonstrating their effectiveness.

Findings

MobileNetV3+ Custom Siamese model achieves top accuracy

Contrastive loss improves model performance

Transformer models perform competitively in scribe verification

Abstract

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.