The digitization and intelligent analysis of ancient Chinese documents face significant challenges due to diverse scripts, complex layouts, and the prevalence of rare characters. We present a comprehensive multi-modal recognition framework developed for the closed-modality track of the EvaHan 2026 Ancient Chinese Document Multi-Modal Recognition Shared Task. Our approach integrates two specialized pipelines to address these complexities. For text recognition (Tasks A and C), we propose a high-precision OCR system based on the domain-adapted Xunzi_Qwen2_VL_7B_Instruct, leveraging DoRA within a two-stage progressive curriculum learning strategy. To further refine character accuracy, DPO is incorporated alongside a dual-adapter architecture for rare character error localization and correction. For layout detection (Task B), we implement DocLayout-YOLO, enhanced by domain-specific pre-training and Mosaic augmentation to achieve efficient NMS-free element detection. Furthermore, a multi-round robust inference strategy, featuring automatic retry mechanisms and multi-prompt brute-force search, is introduced to handle stubborn and degraded samples effectively. Experimental results demonstrate that our proposed framework achieves superior performance across all evaluation metrics, highlighting its robustness and effectiveness in the digital preservation of ancient Chinese heritage.

“近年来,网络科技的飞速发展在为整个社会带来极大便利的同时,也加剧了仇恨言论的传播。仇恨言论可能会构成网络暴力,诱发仇恨性的犯罪行为,对社会公共文明和网络空间秩序造成极大的威胁。因此,对网络仇恨言论进行主动的监管和制约具有重大意义。而当前学术界针对俄语的网络仇恨言论研究不足,尤其缺乏俄语网络仇恨言论语料库,这极大地限制了相关技术和应用的发展。2022年俄乌冲突爆发以后,对于俄语网络仇恨言论语料库的研究与构建显得更加迫切。在本文中,作者提出了一种细粒度的俄语网络仇恨言论语料库构建及标注方案,并基于该方案首次创建了包含20476条文本数据,具有针对性、话题统一的俄语仇恨性言论语料库。”