Multi-Expert Routing for Multi-Domain Low-Resource OCR: A Manchu Case Study

arXiv cs.AI · 18 hours ago

Researchers developed a multi-expert routing system for Manchu historical document OCR that uses a lightweight classifier to direct different document styles to specialized models trained through iterative fine-tuning. The routed system achieved character error rates of 0.30 percent on regular script, 1.57 percent on memorials, and 4.83 percent on running script, matching performance of domain-specific specialists selected for each style. This approach enables effective OCR for low-resource historical documents with multiple visual styles by reusing checkpoints as domain experts rather than training separate models from scratch.