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

2026-07-15

AI Summary

This work addresses the challenges of diverse historical Manchu handwriting styles—such as regular script, running script, and semi-cursive memorial script—and the scarcity of annotated data in optical character recognition (OCR). The authors propose a mixture-of-experts routing system that innovatively repurposes model checkpoints from iterative fine-tuning as domain-specific experts. A lightweight page-level visual style classifier enables highly accurate expert selection, achieving 99.3% routing accuracy, and dynamically instantiates new experts when no suitable one exists. Remarkably, without access to ground-truth style labels, the system attains character error rates of 0.30%, 1.57%, and 4.83% on three test sets, matching the performance upper bound achievable with oracle style labels and substantially improving cross-style Manchu text recognition under low-resource conditions.

Abstract

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.

Problem

• low-resource OCR
• multi-domain
• Manchu script
• handwriting styles
• historical document recognition

Innovation

• multi-expert routing
• low-resource OCR
• domain adaptation
• Manchu script
• checkpoint reuse