The digitization of historical documents is vital for preserving cultural heritage, yet mainstream OCR (Optical Character Recognition) systems often fail to support minority languages due to limited training data and language-specific models. This study explores how open-source OCR frameworks can be adapted to overcome these limitations, focusing on Finnish and Swedish as case studies. We present a practical methodology for fine-tuning PaddleOCR using a combination of manually annotated and synthetically generated data, supported by high-performance computing infrastructure. Our enhanced model significantly outperforms both Tesseract and baseline PaddleOCR, particularly in recognizing handwritten and domain-specific texts. The results highlight the importance of domain adaptation, GPU acceleration, and open-source flexibility in building OCR systems tailored for under-resourced languages. This work offers a replicable blueprint for cultural institutions seeking locally deployable OCR solution.