Digital humanities research has focused primarily on the analysis of texts. This emphasis stems from the availability of technology to study digitized text. Optical character recognition allows researchers to use keywords to search and analyze digitized texts. However, archives of digitized sources also contain large numbers of images. This article shows how convolutional neural networks (CNNs) can be used to categorize and analyze digitized historical visual sources. We present three different approaches to using CNNs for gaining a deeper understanding of visual trends in an archive of digitized Dutch newspapers. These include detecting medium-specific features (separating photographs from illustrations), querying images based on abstract visual aspects (clustering visually similar advertisements), and training a neural network based on visual categories developed by domain experts. We argue that CNNs allow researchers to explore the visual side of the digital turn. They allow archivists and researchers to classify and spot trends in large collections of digitized visual sources in radically new ways.

中文翻译：

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视觉数字化转变：使用神经网络研究历史图像

数字人文研究主要集中于文本分析。这种强调源于研究数字化文本的技术的可用性。光学字符识别使研究人员可以使用关键字来搜索和分析数字化文本。但是，数字化来源的档案也包含大量图像。本文展示了如何使用卷积神经网络（CNN）来分类和分析数字化的历史视觉资源。我们提供了三种使用CNN的不同方法，以使人们对数字化荷兰报纸档案中的视觉趋势有更深入的了解。这些措施包括检测特定于媒体的功能（从插图中分离照片），基于抽象视觉方面查询图像（聚集视觉上相似的广告），并根据领域专家开发的视觉类别训练神经网络。我们认为，CNN可以使研究人员探索数字转弯的视觉效果。它们使档案管理员和研究人员能够以全新的方式对大量数字化视觉资源中的趋势进行分类和发现。