A Parameter-Efficient and Data-Centric Framework for Ancient Chinese Text Recognition and Layout Analysis
Year: 2026
Type: conference-paper
Abstract: This paper presents the system developed for the EvaHan 2026 shared task on Ancient Chinese OCR and Layout Analysis.Participating in the Closed Track, we propose a highly parameter-efficient, data-centric framework based on the Qwen2.5-VL-7B-Instructmultimodal large language model (MLLM).While the official baseline utilizes the same backbone architecture, our approach significantly outperforms it by integrating orientation-aware image preprocessing and expert-constrained adaptive prompt engineering.We employed Low-Rank Adaptation (LoRA) with a minimal rank configuration (Rank=16) to train three independent, task-specific adapters.Our system achieved exceptional results, recording an Overall score of 0.9703 and an F1-score of 97.19% on printed text recognition (Task A)-effectively halving the baseline's Character Error Rate.On handwritten texts (Task C), we maintained a highly competitive 90.18%F1-score.Furthermore, our model achieved significant progress in layout analysis (Task B), surpassing the baseline's Macro F1 by 172% (0.4162 vs. 0.1530) and mAP by 37%.These results underscore that embedding explicit document structure and semantic constraints into MLLMs is more effective than simply scaling model parameters.
Author: Yuchun Meng
Institution: Nanjing University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166526582