III: Small: Collaborative Research: Adaptive Integration of Textual and Geospatial Information for Mining Massive Map Collections

图书馆和档案馆正在将历史地图数字化，以供广泛的在线访问。如果没有搜索它们的技术，与特定问题或问题相关的大型地图集即使在在线档案中也可能仍然模糊不清。如果地图上的所有文字都能被计算机自动读取，那么大量的信息——地名、地理特征，通常还有统计数据——就会很快得到。该项目将通过自动识别这些数字化文物中的地名和其他文本，同时将其与现代地理相结合，从而提高历史地图的搜索和分析能力。该项目将对当前的文本识别方法进行改进，这将有助于更有力地利用人类的古老地图宝库——例如，通过允许科学家和政策制定者建立土地使用、水道或边界随时间的变化。通过创建免费的开源工具来研究历史地图，该项目将增加公众对科学和技术的参与，并使任何互联网用户都能探索技术和历史的交叉点。本研究将训练不同群体的研究生和本科生构建、学习和预测具有异质但高度相互依存实体的自适应模型。尽管许多机构正在对成千上万的历史地图进行数字化，但大多数数字化地图图像都没有得到很好的注释，限制了它们的用途。手工标注和元数据关联非常费力。该项目的主要目标是：(1)完全自动化基于文本和形状的地理参考（将地图图像与已知的全球地理位置对齐），同时(2)通过增强这些复杂工件中的文本检测和识别方法来索引单词和地名（用于搜索）。这些创新将解决人工地理参考和当前自动文本识别算法的缺点。研究人员将采用迭代解释过程来解决包括文本/图形分离、文本识别和地理参考在内的问题。例如，地图上给定文本实体类的所有成员（例如，国家名称）通常以相同的文本样式呈现，这一事实可用于使用从更容易识别的成员中获得的信息来预测该类别中较难识别的成员。研究人员将使用一个带有注释的地图数据集，其中包含9000个地名的12000多个单词，作为测试项目中开发的算法的基准数据。软件、数据和基准将在项目网站（http://www.cs.grinnell.edu/~weinman/research/maps.shtml）上广泛发布。研究结果将通过计算机视觉、人工智能和GIS社区的期刊和会议与研究界分享。

Libraries and archives are digitizing historical maps for widespread online access. Without technology for searching them, large map collections relevant to a given problem or question may remain obscure even in online archives. If all of the text in a map can be read automatically by computer, a wealth of information becomes quickly available -- location names, geographic features, and often statistics. This project will increase capacity for search and analysis of historical maps by automatically recognizing place names and other text in these digitized artifacts while simultaneously aligning them with modern geography. The improvements this project will make to current text recognition methods will facilitate more powerful uses of humanity's trove of old maps -- for example, by allowing scientists and policymakers to establish changes in land usage, waterways, or borders over time. By creating free, open-source tools for studying historical maps, this project will increase public engagement with science and technology and empower any Internet user to explore the intersection of technology and history. This research will train a diverse group of graduate and undergraduate students in constructing, learning, and making predictions with adaptive models featuring heterogeneous yet highly interdependent entities.Although many institutions are digitizing hundreds of thousands of historical maps, most digitized map images are poorly annotated, limiting their usefulness. Manual annotation and metadata association is highly laborious. This project's primary objectives are (1) to fully automate text and shape-based georeferencing (aligning map images to the known global geography) while (2) indexing words and place names (for search) by enhancing text detection and recognition methods in these complex artifacts. These innovations will address the shortcomings of manual georeferencing and current automated text recognition algorithms. The researchers will employ an iterative interpretation process for solving problems including text/graphics separation, text recognition, and georeferencing. For example, the fact that all members of a given class of text entities on a map (e.g., county names) are typically rendered in the same text style can be used to inform predictions about difficult members of the category with information derived from more easily-recognized members. The researchers will use a dataset of annotated maps containing over 12,000 words in 9,000 place names as benchmark data for testing the algorithms developed in the project. Software, data, and benchmarks will be broadly distributed on the project website (http://www.cs.grinnell.edu/~weinman/research/maps.shtml). Findings will be shared with the research community through journals and conferences in the computer vision, artificial intelligence, and GIS communities.