Historical maps are rich cultural artifacts that pose significant challenges for computational analysis. Maps contain a complex assortment of textual and visual elements, including diverse annotations, nonstandard layouts, and numerous symbolic representations. These features complicate the application of conventional optical character recognition (OCR) technologies. This presentation explores the feasibility of using contemporary artificial intelligence (AI) tools to identify handwritten placenames and topographic symbols—such as churches and fortifications—in early modern cartographic materials. The project focuses on the unprinted works of Danish cartographer Johannes Mejer (1606–1674), whose maps can offer unique insight into 17th-century Nordic geography and culture; for instance, Mejer was among the first to chart the region of Skåne prior to its acquisition by Sweden. The corpus comprises over 200 digitized items from the Danish Royal Library, including maps, sketches, and handwritten documents. These copious materials has not been systematically transcribed or analysed thus far, presenting an opportunity to apply AI-based methods to unlock their content. To evaluate the feasibility of currently available AI technologies for handwritten text recognition (HTR) and symbolic image recognition with respect to such historic maps, the project experiments with several AI-drive tools. Initial experiments have been conducted using Transkribus (German Giant model), HTRFlow, and several large language models (LLMs), including ChatGPT, Claude, and Gemini. Transkribus has shown limited success in accurately identifying handwritten placenames embedded within cartographic contexts. LLMs are being evaluated for their potential – already demonstrating impressive results; a testing environment is now under development to facilitate the systematic comparison of performance by different LLMs as well as integration with Transkribus and ground truth. DHNB 2026 — Book of Abstracts 166 This work-in-progress explores feasibility for applying of AI towards complex historical documents. We invite feedback and collaboration from researchers with experience in machine learning, historical cartography, and archival digitization.