Multi-Expert Routing for Multi-Domain Low-Resource OCR: A Manchu Case Study
Reading ancient Manchu documents by matching each page to the right AI specialist
Researchers built a system that automatically sorts pages of historical Manchu documents by their visual style, then sends each page to the AI reader best suited for that style. The system achieved near-perfect sorting accuracy (99.3%) and matched the performance of a specialist reader for each style, even when some specialists hadn't been specifically trained for their final assignment.
Historical documents in Manchu script exist in multiple distinct handwriting styles that confuse standard OCR systems, and labeled training data is scarce. This routing approach makes it practical to digitize large Manchu archives without needing massive amounts of labeled examples for every style—the system reuses existing trained models intelligently instead. It demonstrates a general technique for low-resource document digitization that could apply to other historical scripts and languages.