The digital preservation and accessibility of historical documents require accurate and scalable Handwritten Text Recognition (HTR). However, progress in this field is significantly hampered for low-resource scripts, such as ancient forms of the scripts used in historical manuscripts, due to the scarcity of high-quality, transcribed training data. We address this critical gap by introducing the AnciDev Dataset, a novel, publicly available resource comprising 3,000 transcribed text lines sourced from 500 pages of different ancient Devanagari manuscripts. To validate the utility of this new resource, we systematically evaluate and fine-tune several HTR models on the AnciDev Dataset. Our experiments demonstrate a significant performance uplift across all fine-tuned models, with the best-performing architecture achieving a substantial reduction in Character Error Rate (CER), confirming the dataset’s efficacy in addressing the unique complexities of ancient handwriting. This work not only provides a crucial, well-curated dataset to the research community but also sets a new, reproducible state-of-the-art for the HTR of historical Devanagari, advancing the effort to digitally preserve India’s documentary heritage.