满文OCR因为书写风格多变很难做，这篇论文用了个巧妙的办法：训练几个专家模型再加个路由分类器，哪个风格用哪个专家，精确到0.3%的错误率。挺实用的思路。
针对满文历史文档多种书写风格（楷书、行书、宫廷奏折半草书）的低资源OCR问题，本研究提出多专家路由系统。该系统复用迭代微调过程中的检查点作为领域专家，并用轻量级页面级图像分类器按视觉风格分配页面。在三个冻结测试集上，路由系统匹配了所选专家，CER分别为0.30%（楷书）、1.57%（奏折）和4.83%（行书）。页面级领域准确率99.3%，与领域标签预言机精度一致。其中两位专家并非专门针对最终领域训练。
Multi-Expert Routing for Multi-Domain Low-Resource OCR: A Manchu Case Study
Historical Manchu OCR must accommodate various visually distinct writing styles, including regular script, running script, and the semi-cursive chancery hand used in palace memorials, despite limited labeled data. We study a multi-expert system that reuses checkpoints from an iterative fine-tuning process as domain specialists and uses a lightweight page-level image classifier to dispatch pages by visual style. When the checkpoint pool lacks a suitable specialist, we train an additional expert for that domain. On three frozen test sets, the routed system matches the selected specialist for each style at two-decimal precision: 0.30 percent CER on regular script, 1.57 percent on memorials, and 4.83 percent on running script. The router achieves 99.3 percent page-level domain accuracy and matches the domain-label oracle at the same precision. Two of the three selected specialists were not trained specifically for their final domain; only the running-script expert was trained with that domain as its target. We report the evaluation protocol, router design, and per-page predictions to make the comparison reproducible.