Manchu, a low-resource language essential for understanding early modern Eastern Eurasian history, lacks effective optical character recognition (OCR) systems that can handle real-world historical documents. This study develops high-performing OCR systems by fine-tuning three open-source vision–language models (Llama-3.2-11B-Vision, Qwen2.5-VL-7B, and Qwen2.5-VL-3B) on 60,000 synthetic Manchu word images using parameter-efficient training. Llama-3.2-11B-Vision achieved exceptional performance with 98.6 percent word accuracy and a character error rate of 0.002 on synthetic data, while maintaining 87.4 percent word-level accuracy (95% CI: 85.1%–89.7%) on scanned word images from formal Qing administrative manuscripts and printed documents. Comparative evaluation reveals substantial advantages over traditional approaches: while a convolutional recurrent neural network baseline achieved 99.9 percent synthetic accuracy, it suffered severe degradation to 56.8 percent on scanned documents. Our approach demonstrates effective synthetic-to-real domain transfer, providing a cost-effective solution deployable on accessible infrastructure. This work establishes a transferable framework for endangered language OCR that reduces technical and financial barriers in digital humanities, enabling historians and linguists to process historical archives without specialized computing resources.