Scanned historical maps in libraries and archives are valuable repositories of geographic data that often do not exist elsewhere. Despite the potential of machine learning tools like the Google Vision APIs for automatically transcribing text from these maps into machine-readable formats, they do not work well with large-sized images (e.g., high-resolution scanned documents), cannot infer the relation between the recognized text and other datasets, and are challenging to integrate with post-processing tools. This paper introduces the mapKurator system, an end-to-end system integrating machine learning models with a comprehensive data processing pipeline. mapKurator empowers automated extraction, post-processing, and linkage of text labels from large numbers of large-dimension historical map scans. The output data, comprising bounding polygons and recognized text, is in the standard GeoJSON format, making it easily modifiable within Geographic Information Systems (GIS). The proposed system allows users to quickly generate valuable data from large numbers of historical maps for in-depth analysis of the map content and, in turn, encourages map findability, accessibility, interoperability, and reusability (FAIR principles). We deployed the mapKurator system and enabled the processing of over 60,000 maps and over 100 million text/place names in the David Rumsey Historical Map collection. We also demonstrated a seamless integration of mapKurator with a collaborative web platform to enable accessing automated approaches for extracting and linking text labels from historical map scans and collective work to improve the results.

翻译：图书馆和档案馆中扫描的历史地图是珍贵的地理数据存储库，这些数据往往在别处无法获取。尽管谷歌视觉API等机器学习工具能够自动将地图中的文本转录为机器可读格式，但它们无法有效处理大尺寸图像（如高分辨率扫描文档），难以推断识别文本与其他数据集之间的关系，且难以与后处理工具集成。本文介绍了mapKurator系统——一个将机器学习模型与综合数据处理流水线相结合的端到端系统。mapKurator能够从大量高维度历史地图扫描件中自动提取文本标签，并进行后处理与链接。其输出数据包括边界多边形和识别文本，采用标准GeoJSON格式，便于在地理信息系统（GIS）中直接修改。该系统允许用户从大量历史地图中快速生成有价值的数据，用于对地图内容进行深度分析，进而提升地图的可发现性、可访问性、互操作性和可复用性（FAIR原则）。我们部署了mapKurator系统，并处理了David Rumsey历史地图收藏中超过6万幅地图和1亿多个文本/地名。我们还展示了mapKurator与协作网络平台的无缝集成，使研究者能够通过自动化方法从历史地图扫描件中提取和链接文本标签，并通过集体工作改进结果。