This paper introduces our system proposal and experimental results for the 5th International Evaluation of Ancient Chinese Information Processing (EvaHan 2026). This evaluation focuses on ancient books OCR tasks using multimodal large language models, including three subtasks: Printed Text Recognition (Task A), Layout Element Analysis (Task B), and Handwritten Text Recognition (Task C). To address core challenges such as numerous variant characters, complex handwritten ligatures, dense layout elements, and annotation noise, we propose a Supervised Fine-tuning (SFT) scheme based on data synthesis augmentation and multi-stage curriculum learning. We also optimized the data preprocessing workflow, resolving key issues like repetition mark recognition and annotation quality improvement. We completed a 9:1 train-validation split on the official dataset and verified the effectiveness of our methods through 6 groups of comparative experiments. Finally, we selected the model with the best comprehensive performance for submission. The code and synthetic dataset are available at https://github.com/zhengningch/EvaHan2026-data.

This paper explores the application of fine-tuning methods based on 7B large language models (LLMs) for named entity recognition (NER) tasks in Chinese ancient texts. Targeting the complex semantics and domain-specific characteristics of ancient texts, particularly in Traditional Chinese Medicine (TCM) texts, we propose a comprehensive fine-tuning and pre-training strategy. By introducing multi-task learning, domain-specific pre-training, and efficient fine-tuning techniques based on LoRA, we achieved significant performance improvements in ancient text NER tasks. Experimental results show that the pre-trained and fine-tuned 7B model achieved an F1 score of 0.93, significantly outperforming general-purpose large language models.