Data retrieval from local heritage books—Is artificial intelligence the solution?

Artificial Intelligence (AI) is rapidly transforming all scientific disciplines. Among its many applications, AI can facilitate data retrieval from a wide range of sources. We evaluate the performance of large language models in extracting data from local heritage books—valuable sources for economic and demographic history. We compare the results of AI-driven, Python code-based, and manual data retrieval for random samples of observations from three heritage books. Our analysis shows that Python code-based retrieval consistently outperforms AI, particularly in minimizing issues such as omitted or hallucinated data. Furthermore, we show that, with minor modifications our Python code-based methods can be adapted to other local heritage books, highlighting the robustness and scalability of this traditional approach.