Digitization projects in humanities often generate vast quantities of page images from historical documents, presenting significant challenges for manual sorting and analysis. These archives contain diverse content, including various text types (handwritten, typed, printed), graphical elements (drawings, maps, photos), and layouts (plain text, tables, forms). Efficiently processing this heterogeneous data requires automated methods to categorize pages based on their content, enabling tailored downstream analysis pipelines. This project addresses this need by developing and evaluating an image classification system specifically designed for historical document pages, leveraging advancements in artificial intelligence and machine learning. The set of categories was chosen to facilitate content-specific processing workflows, separating pages requiring different analysis techniques (e.g., OCR for text, image analysis for graphics)

翻译：人文学科的数字化项目常产生海量历史文档页图像，给人工分类与分析带来重大挑战。这些档案包含多样化内容：多种文本类型（手写、打字、印刷）、图形元素（绘画、地图、照片）及版面结构（纯文本、表格、表单）。高效处理此类异构数据需要自动化方法对页面内容进行分类，从而支持定制化的下游分析流程。本项目通过开发并评估专为历史文档页设计的图像分类系统应对此需求，充分利用人工智能与机器学习领域的最新进展。类别体系的选择旨在支撑内容导向型处理工作流，将需不同分析技术（如文本采用OCR、图形采用图像分析）的页面分开处理。