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LVLM Optimization for Ancient Chinese Book Image Analysis with Task-specific Augmentation and Instruction Tuning
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LVLM Optimization for Ancient Chinese Book Image Analysis with Task-specific Augmentation and Instruction Tuning
Ancient Chinese text digitization faces challenges like variant characters and complex layouts. Based on the EvaHan 2026 tasks, this study proposes an LVLM-based framework for printed/handwritten text recognition and layout analysis. To effectively adapt the Qwen2.5-VL-7B-Instruct model, our methodology innovates through a dual-level optimization strategy: distinct augmentation strategies are developed for OCR and layout tasks, while task-specific prompt templates are engineered to decouple text transcription from coordinate prediction. This combined approach significantly enhances overall task proficiency, achieving Character Error Rates of 0.0372 (printed) and 0.0823 (handwritten), alongside a mean average Precision of 0.2933 for layout analysis. Results show general LVLMs underperform in zero-shot ancient text tasks, but fine-tuning with tailored strategies significantly boosts performance and highlights their potential.
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