Digitization projects in humanities often generate vast quantities of page images from historical documents, presenting significant challenges for manual sorting and analysis.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This project addresses this need by developing and evaluating an image classification system specifically designed for historical document pages, leveraging advancements in artificial intelligence and machine learning.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：人文学科数字化项目常产生大量历史文献页面图像，对人工分类与分析构成重大挑战。这些档案包含多样化内容，涵盖各类文本类型（手写体、打字体、印刷体）、图形元素（绘图、地图、照片）及版面布局（纯文本、表格、表单）。高效处理此类异构数据需要自动化方法，基于页面内容进行分类以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献页面设计的图像分类系统来应对这一需求，该系统融合人工智能与机器学习前沿技术。选定的分类体系旨在适配内容特定处理工作流，将需采用不同分析技术的页面（如文本需光学字符识别，图形需图像分析）进行区分。