Jul 15, 2026 · cs.CVJ/K move · Enter open · S save
Zhan Chen, Jiqiao Ma, Chih-wen Kuo
Institute of Advanced Studies, Beijing Normal University, Zhuhai, China · Faculty of Humanities and Social Sciences, Beijing Normal University–Hong Kong Baptist University United International College (UIC), Zhuhai, China · Independent Scholar · Department of Applied History, National Chiayi University, Chiayi, Taiwan
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.