Multi-Expert Routing for Multi-Domain Low-Resource OCR: A Manchu Case Study· 多领域低资源OCR的多专家路由
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.
研究多领域低资源OCR的多专家路由方案，以识别不同风格的满文。
- 核心方法
- 系统采用轻量级页面级图像分类器根据视觉风格路由页面，并在迭代精调过程中重用检查点作为领域专家，当检查点池中缺少合适的专家时，再针对该领域训练新的专家。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 针对历史满文OCR需求，需要处理不同书写风格（如正楷、行书和宫廷奏折半草书）且数据标注有限的问题。
- 关键实验
- 关键实验包括在正楷、奏折和行书三种风格的测试集上的性能评估，以及页面级领域识别准确率达到99.3%。
- 主要贡献
- 在三个冻结的测试集上展示了高精度的领域识别及字符错误率，使多领域低资源OCR方案具有高可重现性和效率。
- 意义与局限
- 该研究显著提升了多领域低资源OCR的性能，尤其在不同的书写风格上。影响是为历史文献的数字化提供了强有力的支持，但仅依赖于有限的训练数据可能限制其进一步提升。