This paper presents a multi-stage system for the EvaHan2026 shared task, addressing the complex challenges of ancient Chinese optical character recognition (OCR) and layout understanding. For text recognition (Tasks A and C), we adopt parameter-efficient LoRA fine-tuning on the Qwen2.5-VL-7B-Instruct vision-language model (VLM). By directly processing full-resolution long-column images, we preserve critical spatial and contextual integrity without heuristic region cropping. For document layout analysis (Task B), we propose a novel hybrid perception-reasoning paradigm. Instead of relying solely on scaling visual detectors, we decouple localization and understanding: utilizing a YOLO-based ensemble for precise spatial bounding, and casting the VLM as a semantic verifier to eliminate spurious detections. Evaluated on the official unseen test set, our system achieves substantial improvements over the provided baselines, obtaining a 0.0441 Character Error Rate (CER) for printed OCR, a 0.0793 CER for handwritten OCR (including variants), and a 0.5118 mAP@[0.5:0.95] for layout detection. These results demonstrate that integrating VLM-based semantic reasoning into traditional visual detection pipelines is highly effective for multimodal historical document analysis.

We present a multi-strategy Named Entity Recognition (NER) system for ancient Chi-nese texts in EvaHan2025. Addressing dataset heterogeneity, we use a Conditional Random Field (CRF) for Tasks A and C to handle six entity types’ complex dependencies, and a lightweight Softmax classifier for Task B’s simpler three-entity tagset. Ablation studies on training data confirm CRF’s superiority in capturing sequence dependencies and Softmax’s computational advantage for simpler tasks. On blind tests, our system achieves F1-scores of 83.94%, 88.31%, and 82.15% for Test A, B, and C—outperforming baselines by 2.46%, 0.81%, and 9.75%. With an overall F1 improvement of 4.30%, it excels across historical and medical domains. This adaptability enhances knowledge extraction from ancient texts, offering a scalable NER framework for low-resource, complex languages.

Named Entity Recognition (NER) is a fun-damental task in Natural Language Process-ing (NLP), particularly in the analysis of Chi-nese historical texts. In this work, we pro-pose an innovative NER model based on Gu-jiRoBERTa, incorporating Conditional Ran-dom Fields (CRF) and Long Short Term Mem-ory Network(LSTM) to enhance sequence la-beling performance. Our model is evaluated on three datasets from the EvaHan2025 competi-tion, demonstrating superior performance over the baseline model, SikuRoBERTa-BiLSTM-CRF. The proposed approach effectively cap-tures contextual dependencies and improves entity boundary recognition. Experimental re-sults show that our method achieves consistent improvements across almost all evaluation met-rics, highlighting its robustness and effective-ness in handling ancient Chinese texts.